Children start to run after they master walking. How running develops, however, is largely unknown.We assessed the maturity of running pattern in two very young, typically developing children in a longitudinal design spanning about three years. Leg and trunk 3D kinematics and electromyography collected in six recording sessions, with more than a hundred strides each, entered our analysis. We recorded walking during the first session (the session of the first independent steps of the two toddlers at the age of 11.9 and 10.6 months) and fast walking or running for the subsequent sessions. More than 100 kinematic and neuromuscular parameters were determined for each session and stride. The equivalent data of five young adults served to define mature running. After dimensionality reduction using principal component analysis, hierarchical cluster analysis based on the average pairwise correlation distance to the adult running cluster served as a measure for maturity of the running pattern.Both children developed running. Yet, in one of them the running pattern did not reach maturity whereas in the other it did. As expected, mature running appeared in later sessions . Interestingly, mature running alternated with episodes of immature running within sessions. Our clustering approach separated them.An additional analysis of the accompanying muscle synergies revealed that the participant who did not reach mature running had more differences in muscle contraction when compared to adults than the other. One may speculate that this difference in muscle activity may have caused the difference in running pattern. Independent walking is a major developmental milestone for children. In typically developing children it commonly occurs between 9 and 15 months of age . While mIn a previous work we studied 5-9-year-old children . There wAs it turns out, there is no immediate agreement between the chronological age and the maturity of treadmill walking and running patterns . Yet, thTackling such commonalities is a challenge, which may fail when following more traditional routes in studying locomotion, namely from either a sole neuromuscular e.g.,  or a solAnswering the relation between the onset of independent walking and the development of running requires longitudinal assessments spanning several years. To quantify the influence of time since onset of independent walking, one must assess participants at the very onset of independent walking (in fact assesement have to start even before that). And recordings must be frequent enough to properly sample the development of running. We monitored two typically developing children for about three years after their first independent walking steps. We conducted seven experimental sessions during each of which we guaranteed more than 100 running strides when recording leg and trunk 3D kinematics and electromyography (EMG). Using the aforementioned shotgun method that encompasses kinematics and neuromuscular data, we investigated the earliest development of running. Possible mechanisms underlying the coordinated locomotion were explored through muscle synergy analysis and by integrating some of the corresponding outcome parameters in the shotgun approach. We expected this approach to allow for determining the degree of maturity also in very young children who just learned / are learning to run. We expected the development of running maturity to be similar, if not identical, to the onset of independent walking when stratifying its time course.We recruited two children and five adults. The two children  were recruited before taking their first independent steps as part of a larger study . The aduThe first recording session of each child participant took place within 9 days of taking at least four consecutive steps without support. The time of first indpendent steps were relayed by the parents to the researchers.Seven sessions were recorded from first steps (FS) to ∼ 32 months after onset of independent walking for each of the two children (P1 and P2). The initial plan was to record each child every three months from their first independent steps until one year after onset of independent walking with a follow-up every six months from that timepoint. As this was not achieved with the first child, we matched the second child to the spacing of the recordings of the first child. As sketched in The experiments consisted of locomoting overground and on a pediatric treadmill  with either no support or trunk/hand support. During the first session (FS), only walking was recorded. During the subsequent sessions, we recorded both walking and fast walking and/or running. Children were tasked to move from one end of the lab to the other end. They were instructed to either walk or run, sometimes enticed with toys or food. When the child was instructed to run, but the speed was between their normal running and walking speed, then the trial was noted as “fast walking”. For sessions +6, +9, +13, +19, and +32, only the trials labelled as fast walking or running were retained for further analysis.All children sessions, except one, were recorded in the BabyGaitLab of the Department of Human Movement Sciences at the Vrije Universiteit Amsterdam, The Netherlands; the remaining session was recorded at the clinical gait laboratory of the Department of Rehabilitation Medicine at the Amsterdam UMC (location VUmc). The adults were recorded in the laboratory of the Santa Lucia Foundation, Rome, and included in a previous publication .The children were barefoot during all recordings and wore only diapers or underpants. When locomoting overground, they were encouraged by researchers/parents to walk/run in a straight line. Sometimes they were supported by handhold. When on the treadmill, the speed was adjusted to a comfortable speed and type of locomotion . On the treadmill, the children were supported on trunk or by handhold by the researcher/parent. Adults were running at 7 and 9 km/h on a treadmill  wearing shoes.2), placed at the approximate location of the muscle belly on the cleaned skin, with interelectrode spacing of ∼1.5 cm. The placement followed the SENIAM recommendations  with 10 (12 for the session recorded at VUmc) infrared cameras affixed to the ceiling, sampled at 100 Hz and one (four for the session recorded at VUmc) video camera  sampled at 100 Hz (50 Hz for the session recorded at VUmc). Reflective markers (14 mm) were placed bilaterally on the acromion (SHO), iliac crest (IL), greater trochanter (GT), lateral femur epicondyle (LE), lateral malleolus (LM) and fifth metatarsal (VM). For each session, a static trial was recorded where the participant was standing still to be used for correction of the joint angles. We recorded electromyography (EMG) bilaterally from the following 16 muscles: tibialis anterior (TA), medial gastrocnemius (MG), lateral gastrocnemius (LG), soleus (SOL), rectus femoris (RF), vastus medialis oblique (VMO), vastus lateralis oblique (VLO), semitendinosus, biceps femoris (BF), tensor fascia latae (TFL), gluteus maximus (GLM), erector spinae level L2 (ES), latissimus dorsi, trapezius, deltoid, and biceps brachii. EMG was recorded using mini-golden reusable surface EMG disc-electrode pairs  provided to the children during treadmill trials were estimated as the percentage reduction of the mean vertical forces compared to  walking . Thus, oData acquisition has been described previously in v), leg length (l), and the gravitational constant (g) using: Fr = v2/(g ⋅ l). Kinematic parameters were calculated based on the 3D kinematics of the lower legs and trunk. The body was modeled as an interconnected chain of rigid segments: SHO-IL for the trunk, IL-GT for the pelvis, GT-LE for the thigh, LE-LM for the shank, and LM-VM for the foot. The main limb axis was defined as the virtual line connecting GT and VM. Joint and elevation angles were generated accordingly. A total of 101 parameters were estimated for each gait cycle using a custom-written algorithm  in the children. For the adults, foot contact was defined as the local minima of the heel marker and foot off as the lift-off of the VM marker by 2 cm from the minimum detected at stance . Strideslgorithm  to provind-order bi-directional Butterworth around k⋅50 Hz, k = 1,…,10, with half-bandwidth of 0.5 Hz), the EMG data were rectified using the modulus of the analytic signal and finally low-pass filtered  to obtain the corresponding EMG envelopes  muscles TA, MG, LG, SOL, RF, VMO, VLO, BF, TFL, GLM, and ES were retained for further analysis. EMG data were visually inspected, and artifacts were removed using a custom-written burst-detection algorithm . After hnvelopes . These e6 iterations, and a completion threshold of 10–6) to account for missing strides (C) and weighting coefficients (W):Before applying the muscle synergy analysis, the amplitude of the EMG activity was normalized to the mean activity for each muscle, interpolated in case of missing values (for a maximum of 50% of the stride) within one stride, and finally concatenated for each session in a [#strides × #samples] × #muscles matrix ([n × 200] × 11). Post-hoc analysis of the interpolation revealed that the mean interpolation was 4.5% for P1 (range: 0.5%-19.5%) and 4.6% for P2 (range: 0.5%-34%) in approximately 22% and 22.6% of the total number of strides, respectively. The gaps were not necessarily consecutive. No interpolation was done on the adult data. Muscle synergies were calculated using weighted non-negative matrix factorization (WNMF) algorithm . For the synergy analysis, the grand average of all strides for each synergy was determined per session.To quantify differences in the duration of the temporal activation patterns of the muscle synergies, we estimated the full width at half-maximum (FWHM) per activation pattern and stride. Here we first subtracted the minima of the activity patterns – in the case of several peaks, the FWHM was calculated for the main peak, i.e., the peak with the highest amplitude and in case of boundary peaks, an assumption was made that the shape of the peak was symmetric . Timing t = 200 samples). The FWHM and the CoA for the extracted synergies were retained and added to the list of gait parameters for further analysis to have a spatial as well as temporal measure for the activation patterns of the muscle synergies  were included. We chose for this ‘blind’ approach as our previous research revealed that in very young children the presence of a flight phase is not a solid indicator for the presence of running . We empln = 107 parameters.For every participant, parameters were combined in a [(number of sessions × number of strides) × number of parameters] matrix [1730 × 107] and z-scored prior to PCA, see Finally, we applied hierarchical clustering with correlation distance . We firsMeans and standard deviations are provided unless otherwise specified. To investigate whether the dimensionless speed Fr and the FWHM of the muscle activation patterns were different between sessions for each participant and comparable to the adults, we used a non-parametric test, the Kruskal-Wallis test, as the data were not normally distributed, confirmed using a Kolmogorov-Smirnov goodness-of-fit hypothesis test. If a statistically significant effect was found, a Bonferroni correction was applied to account for multiple comparisons. With 7 mixed within-between participants (6 sessions for the toddlers and 1 session for the adults), the used significance threshold was α=0.05/7=0.007.Our child participants were comparable in terms of age as well as time in months since first independent steps. P2 started walking at 10.6 months whereas P1 started walking at 11.9 months. Both children were relatively early walkers. The median age of independent walking lies between 11.4 months  and 13.02.2 Setup for further details) were analyzed.The first sessions, the FS sessions, were recorded within 9 days of when the children performed at least four independent strides, and for these sessions only walking was recorded. In the subsequent sessions, the children were instructed to either walk or run, but only strides from trials when instructed to run . The +2 months sessions were excluded due to insufficient data quality or an insufficient amount of data recorded. See The mean number of strides included per session was (mean ± std) 133 ± 73 strides for the toddlers’ sessions and a total of 105 strides for the adults (range 14-24 per participant). The FS session for P1 had an exceptionally large number of strides that could be included. To make the number of strides more balanced across sessions, only overground strides with a velocity of more than 0.5 km/h were retained, reducing the total number of strides from 729 to 254 strides for this specific session. Several strides with a Froude number exceeding 1.5 were excluded from the sessions +19 and +32 for both P1  and P2  as they were deemed to be sprinting and as such were not comparable to the other data.p < 1 × 10−14 for all sessions for both participants). The normalized speeds ranged between 0.34 and 0.69 for the running sessions and three sessions of P1  and two sessions of P2  were significantly different from the adults, cf. The FS sessions had only strides with double support phases whereas the remaining sessions had a mixture of strides with double support and flight phase. The double support and flight phases were expressed as a percentage of the gait cycle. Double support phases were present in all sessions (after the FS sessions) of P1 and P2, with a tendency towards an increased amount of flight phase in the last sessions. The flight phase was shorter than in the adults. The linear regression of the double support phase revealed a significant effect of session, no effect on participant, and only a small interaction effect between session and participant cf., . The linLike the presence of a flight phase, the Froude number might also not be a good indicator of whether a child is running. The Froude number, the normalized speed, is useful to determine the optimal speed at which to transition from walking to running or vice versa, and in adults this transition occurs at a Froude value of 0.5  which thCI95 except for three. The three parameters not contributing were parameters 74, 79, and 88, i.e., the phase relationship between the two limbs (a measure for interlimb coordination), projection of 1st eigenvector on the shank axis  accounted for > 45% of the total variance of the data. The scatterplots in We found three clusters, see The differences in the developmental trajectories shown in The reconstruction accuracy (RA) revealed different numbers of muscle synergies between sessions with median of three muscle synergies (IQR: 3:4.5) across all sessions. The RA was 64.2 ± 1.8, 64.1 ± 2.6, and 66.3 for P1, P2, and the adults, respectively, for three synergies. The temporal activation patterns of the first of the three synergies had the most activity at foot contact, the weight acceptance phase, with the knee extender muscles  being the predominant influencers both for toddlers and adults with some contribution also of the TFL and GLM muscles cf. . The secp < 0.0001 for synergy 1, +6, +13, +32 were all p < 0.001 for synergy 2) with some in-between significant differences between the sessions within P1 and P2. Synergy 3 of P2 had a characteristic pattern of a reduction of the FWHM from the first running session (+6) to the last running session (+32) with significant p-values of p < 0.0001 for the +6 session compared to +19, +32, and adults and p = 0.0067 for the +6 session compared to the +13 session.FWHM and CoA mark the ability to quickly contract a muscle and the timing of the muscle contraction, respectively. The FWHM of the temporal patterns of the three synergies was comparable across sessions with a large variance between strides cf. . The mosp < 6 × 10−4 and p < 2 × 10−16, respectively). Here, the adult patterns were similar with a mean of 13.3% ± 3.6% of the gait cycle for the second component.The CoA of the synergies were way more variable in the children than in the adults. In the latter the CoAs were within the same 25% of the gait cycle across strides . For the first and third pattern, the range of the CoA for P1 and P2 covered all percentages of the gait cycle for all sessions. Most noteworthy is the mean CoA for the second temporal pattern which occurred later in the FS sessions (31.6% ± 18.5% and 56.3% ± 18.7% of the gait cycle) for P1 and P2, respectively, compared to the mean over the other sessions, which ranged from 13.8% to 16.0% for P1 and 13.6% to 20.2% for P2 , already in PC1-PC2 space the first step session (very dark blue) was indeed close to the adults. In a future study we will compare new walkers to very experienced runners. There we will also include adult walking, i.e., experienced walking, and expect that the FS strides will becloser to this cluster than to adult running.In the current two children we cannot pinpoint a moment when the running pattern differs from the walking pattern and becomes like the adult running pattern. This is mostly due to a substantial variability between and within sessions in the majority of outcome variables. We would like to stress anyway that we were able to show that the two toddlers seemingly display different paths towards running maturity.We determined three muscle synergies. This number is smaller than in many other studies on running where the number is usually four or more . We chosSynergy 2 shows a phase shift as evidenced in the CoA from 30-50% of the gait cycle for the FS session to around ∼15% of the gait cycle for the later sessions. The phase shift occurs from the +6 session and is stable across the running sessions, which indicates that a contraction of the foot flexors matching the shorter stance phase is important for even early running patterns.The analysis of FWHM of the temporal activation patterns of the muscle synergies allowed us to quantify the duration of activity of the muscles contributing to this particular synergy. FWHM of motor primitives or muscle synergies has previously been hypothesised to be a measure for robustness of the motor control . For botThe EMG was processed by normalizing the amplitude to the mean of the data across strides. According to The main limitation of this study is the lack of power by only having two participants. Without a doubt this limits the ability to generalize our results. Despite the lack of generalizability, we consider it a valuable starting point, first in methods and – more importantly, in clarifying that time since onset of independent walking does not appear to be a solid indicator of the maturity of running patterns in very young children.The two clusters, C3 “walking” and C2 “immature running” were so close to each other that they are just overlapping in terms of our measure of maturity, the mean pairwise correlation distance to that of the adults. This may suggest that a cluster solution with three clusters may not have been optimal. However, the mean pairwise distance to the adults did not change with an altered number of clusters. We are hence convinced that our measure can be used for determining the “order of maturity”. On the other hand, the FS session of P1 can be considered more mature than +6 and +9, two sessions containing running strides. Apparently, the distance measure and linkage method used to create the dendrogram was less optimal than in our previous study . HoweverFuture research should be focused on investigating larger number of children. When doing so, we advocate combining many kinematic and neuromuscular parameters to fully investigate the development of the running patterns in very young children.Our study is unique in that the development of running was monitored longitudinally over a three-years span with highly frequent assessments of kinetics, kinematics, and electromyography. It provides a first view on the effect of time since onset of independent walking on the development of running. Running development followed different trajectories that we quantified ‘blindly’ via a shotgun approach after combining various biomechanical and neuromuscular parameters. Evidently, the development of running can take different trajectories including the co-existence of immature and mature running within the same session in a child. Muscle synergy analysis may help explaining why the development can differ between children, though there is a long way to clarifying this with statistical robustness.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Scientific and Ethical Review Board of the Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Netherlands (File number: VCWE-2016-082) and the Ethics Committee of the Santa Lucia Institute, Rome, Italy (Prot. CE-AG4-PROG.99-155). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Written informed consent was obtained from the minor(s)’ legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article.MB: conceptualization, formal analysis, investigation, data curation, writing – original draft, writing – review and editing, and visualization. CZ: conceptualization, investigation, and writing – review and editing. GC and FL: investigation and writing – review and editing. YI: investigation, funding acquisition, and writing – review and editing. AD: writing – review and editing, supervision, and visualization. ND: conceptualization, formal analysis, investigation, writing – review and editing, supervision, and funding acquisition. All authors contributed to the article and approved the submitted version.


Hypoxia is a sentinel feature of idiopathic pulmonary fibrosis (IPF). The IPF microenvironment contains high lactate levels, and hypoxia enhances cellular lactate production. Lactate, acting through the GPR81 lactate receptor, serves as a signal molecule regulating cellular processes. We previously identified intrinsically fibrogenic mesenchymal progenitor cells (MPCs) that drive fibrosis in the lungs of patients with IPF. However, whether hypoxia enhances IPF MPC fibrogenicity is unclear. We hypothesized that hypoxia increases IPF MPC fibrogenicity via lactate and its cognate receptor GPR81. Here we show that hypoxia promotes IPF MPC self-renewal. The mechanism involves hypoxia-mediated enhancement of LDHA function and lactate production and release. Hypoxia also increases HIF1α levels, and this increase in turn augments the expression of GPR81. Exogenous lactate operating through GPR81 promotes IPF MPC self-renewal. IHC analysis of IPF lung tissue demonstrates IPF MPCs expressing GPR81 and hypoxic markers on the periphery of the fibroblastic focus. We show that hypoxia enhances IPF MPC fibrogenicity in vivo. We demonstrate that knockdown of GPR81 inhibits hypoxia-induced IPF MPC self-renewal in vitro and attenuates hypoxia-induced IPF MPC fibrogenicity in vivo. Our data demonstrate that hypoxia creates a feed-forward loop that augments IPF MPC fibrogenicity via the lactate/GPR81/HIF1α pathway. Idiopathic pulmonary fibrosis (IPF) is a lethal progressive fibrotic lung disease, where the unrelenting scarring of alveolar gas-exchange units results in progressive hypoxia and death by asphyxiation –6. AlthoIn a hypoxic environment, lactate production increases as the anaerobic cycle is utilized for energy production. While lactate has long been known to play an essential role in cellular metabolism, more recently, lactate has been discovered to serve as a signal molecule regulating a variety of cellular processes associated with cancer progression and cardiovascular disease –15. In tHere, we show that the highly cellular region at the periphery of the fibroblastic focus contains IPF MPCs expressing hypoxic markers. We show that, under normoxic conditions, the intrinsically fibrogenic IPF MPCs display increased levels of markers of glycolysis and lactate compared with control MPCs. Importantly, we demonstrate that hypoxia markedly enhances lactate production and release in IPF MPCs. We show that hypoxia increases HIF1α levels. In turn, HIF1α binds to the lactate receptor GPR81 promoter, thereby increasing the expression of GPR81, suggesting that, in response to hypoxic conditions, HIF1α promotes GPR81 expression via increasing GPR81 transcription. We demonstrate that exposure to hypoxic conditions promotes IPF MPC self-renewal. Similar to hypoxia, exogenous administration of lactate promotes IPF MPC self-renewal. Knockdown of GPR81 abrogates hypoxia-induced IPF MPC self-renewal in vitro, and loss of GPR81 function markedly diminishes IPF MPC fibrogenicity in both a humanized mouse model of sustained interstitial fibrosis and in a zebrafish fibrosis model. Our results indicate (a) that cells on the periphery of the fibroblastic focus display markers of hypoxia that codistribute with IPF MPCs and (b) that hypoxia creates a feed-forward loop that enhances IPF MPC fibrogenicity via augmenting lactate production, release, and signaling via the GPR81 lactate receptor. These results identify the lactate/GPR81 axis as a potential therapeutic target to arrest IPF fibrotic progression.We previously discovered that the IPF lung contains intrinsically fibrogenic MPCs that cause nonresolving interstitial lung fibrosis in a humanized mouse xenograft model . We foun2 concentrations vary widely in tissues, at the cellular level, physiological O2 concentrations are generally in the 1%–11% range . These data indicate that IPF MPCs, which are intrinsically fibrogenic, display increased glycolysis and higher levels of lactate compared with control MPCs under normoxic conditions and that hypoxia markedly enhances intracellular lactate levels in IPF MPCs.Since hypoxia enhanced IPF MPC self-renewal, we next sought to elucidate the mechanism. Prior work indicates that IPF lung tissue contains high lactate levels  and thatSince hypoxia enhances lactate production and release in IPF MPCs, we next examined the effect of exogenous lactate  on IPF MPC self-renewal. Compared with no exogenous lactate, both 5 mM and 10 mM lactate stimulated IPF MPC self-renewal, while IPF MPC self-renewal was slightly lower with 20 mM lactate . TherefoWe next examined the effect of LDHA loss of function on IPF MPC lactate levels and self-renewal in response to hypoxia. Knockdown of LDHA decreased LDHA expression and activity and reduced lactate levels in IPF MPCs in response to hypoxia . Loss ofOur in vitro data indicate that hypoxia enhances lactate production and release and exogenous lactate promotes IPF MPC self-renewal. GPR81 is the cognate receptor for lactate, and lactate signaling through GPR81 can regulate a variety of cellular processes –19. Ther+ MPCs expressing GPR81 were present on the periphery of the fibroblastic focus  pathway and target the cAMP response element-binding protein (CREB) to affect cell function. To begin to assess the pathways downstream of GPR81 that affect IPF MPC self-renewal, we quantified PKA and phosphorylated CREB (pCREB ser133) levels in IPF MPCs in response to exogenous lactate. Lactate increased both PKA and pCREB levels . FurtherThe HIF1α transcription factor is a major regulator of protein expression in response to hypoxia , 43. Sin2) conditions (10% was the lowest O2 concentration permitted by the University of Minnesota IACUC). Lungs were harvested 4 weeks later. LDHA activity as a surrogate marker of lactate production was quantified. LDHA activity in the lungs of mice transduced with scrambled shRNA and exposed to hypoxia was increased compared with mice exposed to normoxia  mice pretreated with intratracheal bleomycin results in nonresolving interstitial lung fibrosis . To initWe next quantified the level of fibrosis in mice treated with IPF MPCs and exposed to normoxic and hypoxic conditions. We found that the collagen content and lung fibrosis score in mice receiving IPF MPCs transduced with scrambled shRNA and exposed to hypoxia were increased compared with mice receiving IPF MPCs transduced with scrambled shRNA and maintained under normoxic conditions  comparedWe next compared the fibrogenicity of IPF MPCs transduced with GPR81 to those transduced with scrambled shRNA in the humanized mouse model under hypoxic conditions. The collagen content and lung fibrosis score were significantly lower in mice receiving IPF MPCs in which GPR81 had been knocked down and exposed to hypoxia compared with mice receiving IPF MPCs transduced with scrambled shRNA and exposed to hypoxia  comparedTo quantify the number of human cells in mice receiving IPF MPCs transduced with GPR81 shRNA or scrambled shRNA, at the time of engraftment and at lung tissue harvest, we used a real-time PCR method shown to be sensitive for the detection and quantification of human cells in mice . The lun2 (the concentration of O2 used in the in vivo studies) on induction of apoptosis in vitro by quantifying caspase 3 levels. No significant change in caspase 3 levels was found  or normoxic conditions for 48 hours. The lungs were harvested and stained with Hydroxyprobe . Cell lines were derived from lungs, characterized as mesenchymal cells, and cultivated as previously described , fulfilling diagnostic criteria for IPF including a pathological diagnosis of usual interstitial pneumonia  at the tescribed .+ and CD44+   were collected. To generate sufficient numbers of MPCs for the in vivo mouse studies, SSEA4+CD44+ cells were expanded by culture in DMEM + 10% FCS for 7 days prior to use. The resulting MPC cultures were reanalyzed for SSEA4 expression by FACS analysis and for colony formation in vitro. In total, 97% of day 7 MPCs were SSEA4+ and formed colonies in methylcellulose indicating retention of progenitor self-renewal properties during in vitro expansion.For isolation of IPF and control MPCs, primary IPF mesenchymal cells were labeled with mouse anti–human SSEA4 antibody conjugated to Alexa Fluor 647  and mouse anti–human CD44 conjugated to FITC . Cells were sorted on a FACS Aria Cell Sorter (BD Biosciences). Cells that were SSEA42) or normoxic (21% O2) conditions for the duration of the experiment. For lactate stimulation, lactate (MilliporeSigma) in the indicated amounts was added to the media. Cells were cultured for 24 hours for most experiments, except where indicated.IPF or control MPCs were cultured under hypoxic . Briefly, IPF MPCs were cultured in RPMI-1640 for 18 hours and were then washed twice with PBS, followed by incubation for 30 minutes in basic RPMI-1640 . The cells were next cultured in Krebs-Ringer-Phosphate-Hepes (KRPH) buffer for 40 minutes, followed by incubation with the glucose analog 2-deoxyglucose (2-DG) for 20 minutes. Following steps per the manufacturer’s instructions, 2-deoxy-D-glucose-6-phosphate (2DG6P) levels were quantified at 420 nm with a SpectraMax M3 microplate reader (Molecular Devices). Lactate levels in IPF MPC lysates and conditioned medium were quantified using a lactate assay kit, per the manufacturer’s instruction (Abcam).2) or normoxic (21% O2) conditions. Enumeration of colonies was performed microscopically, and colony size was quantified by ImageJ (NIH). In some self-renewal assays, the cells were treated with the indicated concentrations of lactate (MilliporeSigma).Single-cell suspensions of IPF MPCs were incorporated into methylcellulose gels (Stemcell Technologies) and were maintained in MSC SFM CTS (Thermo Fisher Scientific) for 1 week under hypoxic (2% O4 IPF MPCs were added to the top of a filter insert (8 μm pore size) containing 100 μL of serum free DMEM in a 96-well plate. A total of 150 μL of DMEM was added to the lower chamber, and the cells were allowed to migrate for 16 hours under hypoxic (2% O2) or normoxic conditions (21% O2). The number of migrating cells were quantified using CyQuant GR dye and a fluorescence plate reader (SpectraMax M3). Alternatively, in some experiments, DMEM containing lactate was added to the lower well and migration was assessed.The effect of hypoxia or lactate (MilliporeSigma) on the migration of IPF and control MPCs was examined using the QCM Chemotaxis Cell Migration Assay Kit following the manufacturer’s instructions (EMD Millipore). Briefly, 2 × 10PLUS SYBR Green I Kit (Roche). Primer sequences were as follows: LDHA Forward: 5′-GCCCGACGTGCATTCCCGATTCCTT-3′; LDHA Reverse: 5′-GACGGCTTTCTCCCTCTTGCTGACG-3′; GPR81 Forward: 5′-AATTTGGCCGTGGCTGATTTC-3′; GPR81 Reverse: 5′-ACCGTAAGGAACACGATGCTC-3′; HIF1α Forward: 5′-TTGATGGGATATGAGCCAGA-3′; HIF1α Reverse, 5-TGTCCTGTGGTGACTTGTCC-3′; GAPDH Forward: 5′-TGTTGCCATCAATGACCCCTT-3′; and GAPDH Reverse: 5′-CTCCACGACGTACTCAGCG-3′.LDHA, GPR81, and HIF1α gene expression was conducted by quantitative PCR (qPCR) as previously described . Total RSamples were quantified at the log-linear portion of the curve using LightCycler analysis software and compared with an external calibration standard curve.For loss of function, LDHA, GPR81, and HIF1α were knocked down using shRNA . Scrambled shRNA served as control. Cells were transduced with LDHA, GPR81, or HIF1α shRNA or scrambled shRNA with 5 ng/mL of Polybrene (MilliporeSigma).g for 15 minutes at 4°C, and the lysates were precleared for 1 hour at 4°C with protein A/G beads and immunoprecipitated for 2 hours at 4°C with the appropriate primary antibody.Western blots were performed as previously described . For imm× 106 cells (with the treatment indicated or vehicle control) were used for chromatin fragment preparation. The experiment was conducted following the manufacturer’s instruction. ChIP-PCR was performed using GPR81 forward primer 5′-CAAGGCAGTCAGGCAAAG-3′ and GPR81 reverse primer 5′-CTTGTTTGTCCTCCCTCCT-3′.ChIP Assay kit  was used. In total, 6 IHC was performed on 4 μm paraffin-embedded serial sectioned IPF lung tissue and mounted on polylysine-coated slides. The sections were deparaffinized in xylene, rehydrated through a graded Methanol series, quenched with 0.3% hydrogen peroxide in methanol, and immersed in a 98°C water bath for 30 minutes in citrate buffer (pH 6.0) for antigen retrieval. Sections were placed in 5% normal horse serum (Jackson ImmunoResearch) to block nonspecific binding of secondary antibodies. A multiplex IHC kit was used for antigen detection according to the manufacturer’s instructions . The tissue specimens were incubated overnight  with the following primary antibodies: anti–rabbit GPR81 monoclonal antibody (1:800) , anti–human CAIX antibody (1:500) , and anti–human SSEA4 antibody (1:100) . Specimens were cover-slipped with a Prolong Antifade Kit (Invitrogen/Molecular Probes) and stored overnight at room temperature without light before image analysis.To assess IPF MPC fibrogenicity in vivo, we utilized 2 assays. Antibodies are listed in Cells stained with a vital dye  were grafted into the central portion of the zebrafish embryo blastoderm at the oblong-sphere stages as previously described. Embryos were immobilized with Tricane after 48 hours. Fluorescence and corresponding bright-field imaging of the PKH26-stained grafts in live embryos were performed with a Zeiss Axiovert upright microscope (Carl Zeiss). After image acquisition, embryos were fixed with 4% paraformaldehyde, infiltrated with increasing concentrations of sucrose/PBS, and cryoembedded in OCT. The size of the fibrotic reticulum was quantified as previously described .× 106 cells/100 μL) and injected via tail vein. Following cell administration, the mice were maintained under normoxic (21% O2) or hypoxic conditions  for the duration of the experiment. Mice were euthanized 4 weeks after adoptive transfer of human cells, and the lungs were harvested. Collagen content was quantified in the left lung tissue by Sircol assay and served as the primary endpoint. Histological (H&E and trichrome staining) and IHC analysis was performed on paraffin-embedded and frozen right lung tissue. The presence of lung fibrotic lesions by histological analysis served as the secondary endpoint. Cells positive for human procollagen  were identified as human. IHC using the human procollagen antibody and GPR81 antibody was performed to assess the distribution of GPR81-expressing cells with human procollagen I expressing cells. The antibody used does not cross-react with mouse collagen.NSG male and female mice (The Jackson Laboratory), average 10 weeks of age, were administered low-dose intratracheal bleomycin (1.25 U/kg) . Two wee× 106 IPF MPCs was used as reference control in qPCR.Mice were euthanized 4 weeks after adoptive transfer of human cells, and the lungs were harvested. The lungs were digested, and genomic DNA was isolated using a PureLink Genomic DNA Mini Kit according to the manufacturer’s instructions (Invitrogen). Real-time PCR was used to quantify human IPF cells in the mouse lungs by measuring the amount of human-specific DNA sequence using human specific primers per a previously published protocol . PCR asst test and 1-way ANOVA, as indicated. P < 0.05 was considered significant.Comparisons of data among experiments were performed using the 2-tailed Student’s Deidentified patient samples were obtained by our tissue procurement service (Bionet) under a waiver of informed consent from the University of Minnesota IRB (no. 1504M68341). Fresh IPF lungs were obtained at the time of lung transplantation, and control lung tissue was obtained by VATS biopsy or lobectomy. Animal protocols were approved and conducted in accordance with the University of Minnesota IACUC regulations .LY conceived, designed, and directed the studies with input from PBB and CAH. LY and CAH wrote the manuscript with assistance from all the authors. LY established primary human mesenchymal cell lines, cultured MPCs, performed flow cytometry for isolation of MPCs, performed qPCR, completed Western blot analyses, and performed gain- and loss-of-function experiments, mouse studies, and IHC. AG and HX assisted with isolation and culture of MPCs and assisted with mouse studies. ER provided slides for IHC. HX and JH also assisted with IHC analyses. KS designed and constructed expression constructs, and AB performed zebrafish xenotransplantation of MPCs.


We report the formationof nanobubbles on graphene with a radiusof the order of 1 nm, using ultralow energy implantation of noblegas ions  into graphene grown on a Pt(111) surface. Weshow that the universal scaling of the aspect ratio, which has previouslybeen established for larger bubbles, breaks down when the bubble radiusapproaches 1 nm, resulting in much larger aspect ratios. Moreover,we observe that the bubble stability and aspect ratio depend on thesubstrate onto which the graphene is grown (bubbles are stable forPt but not for Cu) and trapped element. We interpret these dependenciesin terms of the atomic compressibility of the noble gas as well asof the adhesion energies between graphene, the substrate, and trappedatoms. Delving into the physicalmechanisms that determine the stability and shape of these subnanometerbubbles reveals that they constitute a fundamentally different regime,exhibiting an extreme aspect ratio, tensile strain, and pressure.The unique properties of this subnanometer regime open an unexploredground for applications of nanobubbles in 2D materials.5 Inthis regime, bubbles have been observed on various substrates  with a variety of trapped substances,15 which do not appear to significantly affect the bubblestability.6 The substrate and trapped substance do affectkey properties, such as shape (in particular the aspect ratio) andthe pressure inside the bubble.6 This dependence is largelydetermined by the balance of the adhesion energies: between grapheneand the substrate (γGS), between the substrate andthe trapped substance (γSb), and between the grapheneand the trapped substance (γGb).5 A particularly striking feature demonstrated for nanobubblesin the few nm regime and larger is that the aspect ratio exhibitsuniversal scalinghmax isthe bubble maximum height, R is the bubble radiusat the base, c1 is a constant (0.7), and Y is the Young modulus.5 Here,by combining scanning tunneling microscopy (STM) measurements withmolecular dynamics (MD) simulations and density functional theory(DFT) calculations, we show that this universal scaling breaks downat small R (near 1 nm and below). We also observethat the bubble stability is strongly dependent on the substrate.We interpret these dependencies in terms of the role of the atomiccompressibility of the noble gases as well as of the adhesion energies. Moreover, these nanobubbles are found to induce high levels ofstrain (of the order of 10%) on the overlaying graphene and are predictedby our MD simulations to hold the noble gas atoms under extreme pressures(exceeding 30 GPa).Theproperties of graphene bubbles with a radius of a few nm andlarger are relatively well understood on the basis of elasticity theoryas well as graphene’s elastic properties and its van der Waals(vdW) attraction to the substrate.17 Nanobubbles are formedby implanting noble gas ions , with a kinetic energyof 25 eV, with perpendicular incidence with respect to the surface.Bubbles were found to only form for graphene on Pt(111) . In the following,we will focus on Pt(111) and return to Cu(111) further below whendiscussing how the bubble stability depends on the substrate. Ionimplantation has been previously used to form graphene nanobubblesof noble gases.15 In contrast to the previous studies, where ion beams with energiesof 500 eV and higher were used, our approach is based on ultralowenergy (ULE) ion implantation. Such low energies are crucial to minimizeirradiation-induced damage. Based on our MD simulations , we selected 25 eV  as sufficiently high for a significant fraction of theions to be transmitted through the graphene layer but sufficientlylow to minimize carbon atom displacements . While ULE ion implantation has been previouslyused for doping of graphene  where vacanciesare required , such defects must be avoided in the context of thepresent work so that the intrinsic elastic properties of grapheneare maintained. The graphene bubbles observed in our samples are identifiedas nanometer-scale protrusions on the surface of graphene (grown onPt(111), implanted with the noble gases) as shown in the STM topographiesin Supporting Information). The high structural order of theirradiated surfaces is supported by our atomic-resolution STM measurementson the as-implanted surfaces (Supporting Information). Indeed, Raman spectroscopy measurements show some degree of disorder. Since the selected implantationenergy is below the threshold for vacancy formation , this disorder is likely associatedwith the breaking of C–C bonds without the production of Cvacancies and likely resulting in locally enhanced interaction ofthe Pt atoms at the interface, leading to the subtle defect featuresobserved by STM.Our samples consist of epitaxial graphene grown by chemical vapordeposition (CVD) on epitaxial Pt(111) and Cu(111) thin films grownon sapphire(0001) substrates. Pt(111) 1, not fo Pt(111) S1. In th Pt(111) S1. In th Pt(111) S1. In th Pt(111) S1. In th Pt(111) S1. In th Pt(111) S1. In th Pt(111) S1. In thR values (>1 nm), the aspectratiotends to converge to a constant value of about 0.2, which is in agreementwith the universal scaling previously observed for bubbles with aradius of few nm and larger.5 However,as R approaches the subnanometer regime, the universalscaling breaks down, showing an increase in the aspect ratio and approaching1 for Ne bubbles. From the experimental data in hmax0  andan average value for The radius and aspectratio of each bubble, for the different elements, are plotted in 5 However, as described in the Supporting Information, the effect is still negligible for the bubbles described here andis in fact in the opposite direction (decreases the aspect ratio).The observed breakdown of the universal scaling must therefore originatefrom a different mechanism, namely the existence of a minimum valuefor hmax (hmax0), correspondingto one atomic layer of the trapped gas atoms. As R approaches this regime, hmax becomesa constant value (hmax0), and consequently, hmax/R transits into a ∼1/R dependence. This is illustrated in hmax/R data with the function(hmax/R = c/R), where c (around 3 to 4 Å)is comparable to hmax0. This ∼1/R fit crossesthe value corresponding to hmax/R becomes constant, given by Bending rigidity  over the sample surface. Such nonhomogeneitycan result from the varying (relative) orientation of the grapheneand Pt lattices  as well as possible local variationsin graphene–Pt adhesion due to the subtle graphene disorderobserved in the STM and Raman data, discussed above. Although thispossible effect of subtle disorder on the graphene–substrateadhesion may also play a role in the stability of the bubbles, itdoes not appear to be a dominant effect, since our MD simulationsreproduce well the stability for Pt and instability for Cu withouttaking into account this disorder.This behavior iswell reproduced by our MD simulations of bubbleswith a varying number of trapped atoms  blurs out these differences.Nevertheless, the data strongly suggest that this quantity does dependon the trapped element. Such a scenario can be understood as due toa variation in γ, that is, higher for Ne (γ ≈ 0.13eV/Å2) and for He (∼0.08 eV/Å2) than for Ar (∼0.02 eV/Å2). Taking γGS = 0.25 eV/Å2 for graphene on Pt23 implies that γSb + γGb is of the order of γGS for Ar (giving γ= 0.02 eV/Å2) but significantly smaller for He andNe. In other words, in the bubble configuration, the interaction (ofvdW nature) of the gas atoms with the Pt surface or with the graphenelayer appears to be more repulsive for Ne and He compared to Ar.Letus first consider the R regime,where the Ne bubbles clearly reach higher hmax values than for He and Ar bubbles  for Ne than for He andAr (rvdW), which increasesfrom He, to Ne, to Ar  and only slightly higher forNe (3.5 and 3.16 Å), it is significantly smaller for Ar (3.1and 3.88 Å). This is indeed consistent with the fact that Aris the most compressible of the three elements, followed by He andNe . This is indeed consistentwith our MD simulations. The average z-motion amplitude(averaged over time and over the trapped atoms) obtained from theMD simulations (z⟩in hmax0 to be larger than the (compressed) vdW diameternoble gas atoms.A similar trend is observed in the low- bubbles 2 and hmae andAr 1. Thisie, to Ar 1. Since,e andNe ). In addulations 3, for thns z⟩in 1 is indediameter4b, that diameter4a, by anAsurface) as well as the (projected)area of the base of the bubble (Aprojected). Asurface is the area of the strainedgraphene region, whereas Aprojected wouldbe the area of that region if the bubble would not exist. The tensilestrain can then be estimated as R ≈ 1 nm, we obtain ϵA values of the orderof 10%. More details are provided as Supporting Information. Regarding pressure, according to the general understandingof surface-induced pressure in solids, it scales with the ratio ofsurface area to the volume of the solid phase.24 For the bubbles under consideration here, as R decreases and the atoms inside the bubbles become more monolayer-like,the surface-to-volume ratio (∼Δz–1) increases dramatically, since Δz → 0. One can therefore expect the pressure to also increasedramatically in the limit of small R. Our MD calculationsshow exactly that  is the trace of the virial stress tensor,and Vb is the volume available to thegas atoms. We note that this method, based on the virial stress tensor,is more general and more appropriate in the present case comparedto other methods based on membrane theory and plate theory. The lattermethods are based on elasticity theory, which is valid in the large-bubblelimit, but tends to overestimate the pressure for small bubbles.6 At such high pressures, at room temperature,these noble gases are expected to be in a solid phase or near theirmelting transition, which is around 10 GPa for He,26 5 GPa for Ne,27 and 1.5 GPafor Ar.28 Considering the pressures estimatedhere (R < 1 nm). Our MD simulations are indeed consistent with this expectation(cf. the videos provided as Supporting Information), showing ratherstable ordered atomic arrangements for Ne (Videos V1 and V2) and Ar (V3 and V4) and more disorderedand dynamic arrangements for He (V5 and V6).Thebreakdown of the universal scaling, leading to extreme aspectratios, is likely to be associated with other unusual physical propertiesin these subnanometer bubbles. Although studying such properties indetail is beyond the scope of this Letter, it is worthwhile discussingstrain and pressure as examples. The tensile strain induced on grapheneby the underlying trapped atoms can be estimated from our STM measurementsas follows. From the STM topography of a bubble, one can determinethe surface area of the graphene layer that wraps the three-dimensionalbubble  are immobilized as intercalated species.The remainder is likely to escape via graphene defects .This bubble instability for graphene on Cu is confirmed in our MDsimulations (cf. the videos provided as Supporting Information): Ifa bubble configuration (similar to those in Pt—videos V7 and V8) is givenas the initial state, the time evolution shows graphene peeling offthe Cu surface, resulting in the dispersion of the trapped gas atoms(V9 and V10). This instability can be easily understood as due tothe much weaker adhesion of graphene to Cu (γGS =0.045 eV/Å229) comparedto Pt (0.251 eV/Å223),that is, the Cu–graphene binding is too weak to sustain thehigh pressures associated with the bubbles. In order to assess ifthe gas–metal adhesion (γSb) also plays arole in this stability difference, we used DFT to calculate the adsorptionenergy and the adsorption distance of isolated He, Ne, and Ar atomson Pt(111) and Cu(111) surfaces (Table S4). Although the adsorption energies are indeed larger for Pt thanfor Cu when comparing the gas elements one by one, it still does notexplain the observed difference in stability. For example, the adsorptionenergies of Ar on Cu (for which bubbles are not stable) are largerthan those of He on Pt (for which stable bubbles are observed). Wetherefore conclude that bubbles are not stable on flat Cu terracesdue to the much weaker adhesion of graphene to Cu as compared to Pt.As mentioned above, unlike for Pt, bubblesare not observed onCu flat terraces. It appears that only the atoms that are trappedin defects , which expands evenfurther the range of possible applications. In particular, since thebubble formation is based on ion implantation, our approach is compatiblewith virtually any implanted element, 2D material, and substrate.To conclude, using ULE implantation of noble gas ions , we produced nanobubbles on graphene with varying radius,from few nm down to subnanometer scales. These nanobubbles are stablefor graphene on Pt but not for graphene on Cu. While the bubble aspectratio behaves differently for the different elements, the universalscaling behavior (that was previously established for larger bubbles)breaks down in all three cases, for a bubble radius around 1 nm, asthe bubble height approaches a minimum corresponding to about an atomicmonolayer. We interpret the observed dependencies on the substrateand trapped element in terms of the adhesion energies between thethree constituents: graphene, the substrate, and the trapped noblegas element. Moreover, these nanobubbles are found to induce highlevels of strain (of the order of 10%) on the overlaying graphene.In addition to providing insight on the spatial distribution of thetrapped atoms and its relation to the bubble morphology and stability,molecular dynamics calculations also allowed us to estimate the vdWpressure inside the bubbles, exceeding 30 GPa for the smallest bubbles.These remarkably high strains and pressures illustrate the uniquecharacteristics of this subnanometer bubble regime  compared to the previously studied(larger) nanobubbles. These unique properties offer new opportunities,for example, to study physical states of matter and chemical reactionsunder high (vdW) pressure or electronic phenomena associated withstrain-induced pseudomagnetic fields in graphene. Since the behaviorreported here is largely determined by the adsorption energies betweenthe three constituents ,one can expect similar behavior for other 2D materials (e.g., transitionmetal dichalcogenides such as MoS


Diagnosis of interstitial lung diseases (ILD) is difficult to perform. Extracellular vesicles (EVs) facilitate cell-to-cell communication, and they are released by a variety of cells. Our goal aimed to investigate EV markers in bronchoalveolar lavage (BAL) from idiopathic pulmonary fibrosis (IPF), sarcoidosis and hypersensitivity pneumonitis (HP) cohorts. ILD patients followed at Siena, Barcelona and Foggia University Hospitals were enrolled. BAL supernatants were used to isolate the EVs. They were characterized by flow cytometry assay through MACSPlex Exsome KIT. The majority of alveolar EV markers were related to the fibrotic damage. CD56, CD105, CD142, CD31 and CD49e were exclusively expressed by alveolar samples from IPF patients, while HP showed only CD86 and CD24. Some EV markers were common between HP and sarcoidosis . Principal component analysis distinguished the three groups based on EV markers with total variance of 60.08%. This study has demonstrated the validity of the flow cytometric method to phenotype and characterize EV surface markers in BAL samples. The two granulomatous diseases, sarcoidosis and HP, cohorts shared alveolar EV markers not revealed in IPF patients. Our findings demonstrated the viability of the alveolar compartment allowing identification of lung-specific markers for IPF and HP. Among the non-neoplastic lung diseases, interstitial lung diseases (ILD) represent a wide and heterogeneous group of more than 200 distinct clinical entities, characterized by the specific involvement of lung interstitium that may lead to pulmonary fibrosis .In the case of ILD, identifying the disease in a precise and timely manner can often be challenging for many reasons: pathophysiological disease mechanisms are inherently complex, classification systems and definitions are constantly evolving, overlapping conditions are present within the lung and multiple pathological entities may coexist in the same lung . As a reExtracellular vesicles (EVs) facilitate cell-to-cell communication, which plays a vital role in nearly all physiological and metabolic activities . As a geThe endothelium of the lung contributes significantly to EV circulation due to its high vascular density . In addiOur research group has recently conducted EV analyses in peripheral blood of patients with idiopathic pulmonary fibrosis (IPF) using flow cytometry and showing an altered exosome profile . These fThe present study aimed to compare 37 exosomal surface markers through flow cytometry in BAL from patients affected by IPF, sarcoidosis and hypersensitivity pneumonitis (HP), enrolled at Siena Referral Centre for rare lung diseases. To corroborate these findings, a validation cohort was enrolled from two referral centers for ILDs (Barcelona and Foggia).p = 0.0312), while sarcoidosis patients were younger (p = 0.0215) and with a female predominance than IPF and HP (p = 0.0121). According to respiratory functional assessment at baseline, we observed a moderate reduction of DLCO percentages associated with mild restrictive impairment of lung volumes in IPF patients compared with HP and sarcoidosis (p = 0.0399). Demographic, clinical and immunological data of study and validation cohorts are reported in p = 0.860). CD133/1, SSEA, CD14 and CD2 were differently expressed in BAL samples from the Siena and Foggia cohorts than the Barcelona cohort (p = 0.0455). The expression of CD9, CD63 and CD81 were similar in both cohorts (p = 0.905). From comparative analysis, the expression of EV surface markers was similar in the cohort and validation cohort , indicating a risk of erroneously rejecting the null hypothesis lower than 0.01%.MANOVA analysis between IPF, HP and sarcoidosis associated lambda (0.174) with a Assessing AHC analysis , the HarPCA plots  were perHeat map analysis a showed In the present multicenter study, 37 EV surface markers were detected in the alveolar compartment of ILD patients at diagnosis for the first time. IPF, HP and sarcoidosis patients were sampled for BALs and EVs were phenotyped by flow cytometric analysis. The majority of markers identified on EV surfaces were related to the fibrotic damage on alveolar epithelial cells I and II as well as endothelial cells and fibroblasts, as reported in IPF etiopathogenesis is still unknown. A dysfunctional epithelium can be ascribed to genetics and epigenetics . As a reIt has been hypothesized that HP and IPF have distinct lung fibrosis phenotypes, one being a post-inflammation-induced fibrosis, whereas the second is more related to tissue remodeling and repair ,37.The expression of a number of cell- and inflammation-related markers in our study supports the hypothesis that inflammation plays a significant role in the pathogenesis of HP and sarcoidosis.In HP, T-lymphocyte markers, CD3, were higher than in sarcoidosis and IPF. In BAL samples from sarcoidosis patients, however, the highest expression of CD4 and CD8 was associated with alveolar lymphocytosis, indicating that the disease was active at the time of diagnosis. Sarcoidosis patients have demonstrated increased inflammatory responses in circulating blood monocytes . It is lAlveolar samples from HP patients showed the highest expression of several B-lymphocytes markers, including CD40 (extracellular positivity in lymphoid tissues ) and CD2There are both T and B cells in the lung parenchyma, and their organization into B lymphoid-rich tertiary lymphoid tissues is a hallmark of HP that is closely related to the level of airway inflammation . StimulaIt is important to emphasize that even though our study provides a relevant contribution by highlighting the expression markers of EVs in BAL samples, it has some limitations. First, we did not include other fibrotic ILD in our study that may show a similar clinical course to IPF or HP. Second, it is important to validate our findings in other ILD cohorts from different geographical areas. In addition, the analysis of EV surface markers in different biological samples will be of interest. A total of 83  patients from the rare lung diseases referral center of the University of Siena were consecutively enrolled in the study. A multidisciplinary discussion confirmed the diagnosis of IPF and fibrotic HP in seventeen  and twenty-four  patients, respectively, according to the American Thoracic Society/European Respiratory Society (ATS/ERS) guidelines . InfectiThis study was approved by the regional ethical review board of Siena (C.E.A.V.S.E. Markerlung 17431) and complied with the Helsinki Declaration. All patients provided written informed consent prior to participating in the study. Healthy donors were not enrolled for ethical reasons.Twenty-one patients  referred to ILD clinic at Hospital de la Santa Creu i Sant Pau (Barcelona) were retrospectively enrolled. Eleven patients were affected by IPF, 5 by sarcoidosis and 5 by HP. Forty-four patients  followed at ILD referral center of Foggia University Hospital were consecutively and prospectively enrolled. Thirty-three patients had diagnoses of IPF, 5 of sarcoidosis and 6 of HP. All diagnoses were made according to ATS/ERS international guidelines based on multidisciplinary discussion.Pulmonary function tests (PFT) were performed in all patients for diagnostic purposes. Forced expiratory volume in the first second (FEV1), forced vital capacity (FVC) and diffusing capacity of the lung for carbon monoxide (DLCO) were recorded. They were performed according to ATS/ERS standards  using a Patients were treated with fentanyl 100 mcg and midazolam (3–5 mg) i.v. 15–30 min prior to bronchoscopy. Lidocaine was administered to the larynx and bronchi for topical anesthesia. A Pentax bronchoscope EB15-J10  was inserted through the mouth to avoid blood contamination. Physiological saline with 3 × 50 mL solution was instilled in the middle or lingual lobe. Ten to twenty mL of a pooled BAL sample were collected, as recommended by international BAL Task Force Group guidelines for BAL cellular analysis . BAL samg for 10 min and then the supernatant was stored at −80 °C. According to the manufacturer’s instruction  [g for 30 min at room temperature (RT), then 110 μL of each sample was aspirated and centrifuged at 10,000× g for 45 min at RT. EVs were characterized by MACSPlex Exosome Capture  beads and incubated overnight using an orbital shaker (450 rpm). Then, 500 μL of MACSPlex Buffer was added in each tube and centrifuged at RT at 300× g for 5 min. Accordingly, each sample was then incubated with CD9, CD63 and CD81 for 1 h at RT protected from light on the orbital shaker. The washing step was repeated and EVs from Siena and Foggia University Hospitals were characterized by FACSLyric (BD Biosciences) and analyzed by Kaluza software 2.1 , while EVs isolated at Barcelona Hospital were characterized using a MACSQuant Analyzer 16 Flow Cytometer . According to literature data, the results from the two flow cytometers are accurate and comparable [BAL samples were processed as follow: the samples were centrifuged at 1500× Germany) , 120 μL mparable ,54. The The results were expressed as means and standard deviations (SD). One-way ANOVA non-parametric test  and Dunn test were performed for multiple comparisons. The chi-squared test was used for categorical variables. MANOVA analysis was performed to determine any significant effects of IPF, HP and sarcoidosis diagnoses (explanatory variables) considered in interaction or otherwise with 37 EV surface markers (dependent variables). Wilks’ lambda tests were performed, the lower the lambda associated with explanatory variables, the more important the effect of these variables is on the dependent variables’ combination. Agglomerative hierarchical clustering (AHC) was performed to cluster the three groups of diagnoses based on dissimilarities between the median APC  values of 37 EV surface markers. The dendrogram plot was assessed showing the progressive grouping of the data. Unsupervised principal component analysis (PCA) and heat map analysis were applied to surface expression markers in an explorative approach to identify trends in the data by 2D representation of the multi-dimensional data set.p value less than 0.05 was considered statistically significant. Statistical analysis was performed using GraphPad Prism 9.4 and XLSTAT2021 software.A The findings of our study have demonstrated the validity of the flow cytometric method to phenotype and characterize EV surface markers in BAL samples. In addition, the viability of the alveolar compartment, as a result of the BAL, was demonstrated, which allowed identifying lung-specific markers for IPF and HP. A further outcome of this study was corroboration of the hypotheses regarding the different fibrotic phenotypes in HP and IPF, where the first is related to cell-mediated inflammation-induced lung disease, while the latter is more often attributed to tissue repair and remodeling. The EV markers identified in BAL samples from IPF patients were not revealed in the HP cohort, most of whom were shared with sarcoidosis as two granulomatous ILD.


The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm Designing biocompatible and flexible electronic devices for neuromrophic applications remains a challenge. Here, Kireev et al. propose graphene-based artificial synaptic transistors with low-energy switching, long-term potentiation, and metaplasticity for future bio-interfaced neural networks. A promising approach for overcoming the power and latency shortfalls of traditional computing is through massively parallel neuromorphic systems3. A wide variety of devices have been proposed to build such systems, from mature technologies such as metal-oxide5 and phase change memories7 to emerging devices such as electrochemical8 and magnetic memories12. Most of these systems, however, employ rigid materials, making them less suited for direct integration with biological matter. Direct interfacing of artificial neuromorphic systems with biological living neurons is a highly ambitious goal, which in the long term may lead to effective brain implants and creation of artificial tissue. There have recently been device innovations to this end utilizing organic materials such as maltose-ascorbic acid13, zein14, PVA15, and cellulose16 but with various deficiencies in synaptic performance. Two-dimensional (2D) materials are a promising material class for bioelectronics18 and neuromorphics21 due to their unique electronic properties and atomically thin structure, allowing for imperceptible interfacing with tissue. However, existing 2D-material based neuromorphic systems often employ elements that would induce toxicity when interacting with biological systems, e.g., involving elements like Li+ ionic carriers23 which directly impact the nervous system24. Other 2D carbon-based systems have non-ideal conductance responses that negatively impact performance in neuromorphic applications27. As a result, new device innovations are needed for future neuromorphic computing solutions that can directly integrate with biological tissue.As the world becomes more interconnected and data-driven, effective deployment of data-intensive computation methods becomes more critical. Large and complex data structures require constant extrapolation, interpolation, and classification, which are ill-suited for memory-constrained von Neumann architectures28 with mobile islands of positively charged water/proton clusters  at the Nafion-graphene interface, yielding high-precision conductance states for synaptic operation in artificial neural networks. Altogether, the proposed devices feature favorable synaptic characteristics and energy efficiency down to 50 aJ/µm2. We evaluate this behavior through neuromorphic simulations on several classification tasks using a prototypical neural network and show that the BLAST devices feature metaplasticity that allows online learning performance exceeding ideal linear, numerical synapse results.The biocompatible graphene-based artificial synaptic transistors (BLAST) introduced in this work are a combination of two flexible, soft, and biocompatible elements: Nafion and graphene. Nafion plays the role of a solid polymeric electrolyte made of a negatively charged polysulfonated backboneers Fig. . When Na2 in area) and microscale . The mBLASTs were fabricated using 180 µm thick Nafion-117, adhesive conductive gold tape to form source and drain electrodes, and graphene electronic tattoos32 supported by 200 nm thick PMMA (details in Methods). It is important to note here that both graphene and Nafion are known to be biocompatible substrates for cellular interfaces. Since a few decades of its discovery, Nafion has been studied for biocompatibility as a bare substrate and has also been widely accepted as a coating material for numerous in vivo biosensing applications35. Graphene, despite the original claims of cytotoxicity, has gained trust in the biomedical research community as a highly biocompatible substrate. Unlike the nm-sized flakes of exfoliated graphene, CVD-grown monolayer graphene supported by a substrate is known to not alter any cellular function36, and often serves as an advantageous substrate for cell growth39. To make mBLASTs, we leverage the previously developed large-scale graphene electronic tattoos (GETs) that are transferred on top of the Nafion, self-adhering and forming a tight interface between the graphene and Nafion. The water-assisted transfer, though rapid (<10 s), leads to hydration of the Nafion membrane; hence the used Nafion cannot be considered completely dry, leading to relatively increased mobility of proton clusters29. The fabricated devices, as shown in Fig. 2. In a separate experiment, we varied the area of mBLAST devices and recorded the change in properties. The gold electrode used to apply synaptic gate potential through the Nafion typically outsizes the graphene channel itself to ensure that the whole graphene-Nafion interface is used effectively. The device schematics and cross-section can be seen in Fig. In this work, two kinds of BLAST devices were fabricated: macroscale  current pulses cause the semi-mobile positively charged proton-concentrated clusters within Nafion to move toward (away) from the graphene interface, shifting the interface capacitance and affecting the electrical double layer, which consecutively affects the charge carriers’ density and graphene channel conductance. To corroborate the hypothesis, we gathered transfer curves of the BLAST devices, shown in Fig. entclass1pt{minima43. Since the Nafion is not fully hydrated and there is no proton source at the gate, there are no freely moving protons to deploy30. Hence, the observed effect is related to the hybrid interface between graphene and Nafion. We hypothesize that the proton clusters migrate toward the graphene-Nafion interface, forming an electrical double layer and either hole- or electron-doping graphene, a similar mechanism to that seen in supercapacitors with carbon-based electrodes44. Our observed surface effect requires far fewer protons to accomplish a conductance change than the bulk electrochemical reaction in PEDOT:PSS due to the atomically thin nature of graphene. Coupled with the slow kinetics of the proton cluster movement within the dry Nafion matrix, long-term potentiation can be explained. It should be noted that voltage pulses can also be used in place of current pulses with the addition of a switch at the gate to open the circuit when a pulse is not being delivered, a common technique to enhance state retention in electrochemical devices45. In the case of the BLASTs, current pulses are used due to ease of use in the experimental setup and to promote the linearity and symmetry of synaptic response because the amount of charge delivered to the device mediates the change in conductance. These characteristics will be described in the subsequent section.Figure 46. The conductance evolution behavior of the mBLAST device is linear and symmetric, indicating the potential use in online learning applications. This is evaluated by applying a ramp test: a series of repeated positive and negative spikes cycling the device across its dynamic range. Linearity and symmetry are calculated by collapsing 50 repeated ramping cycles into a single ramp  cannot be concluded due to the appearance of two area-dependent clusters, a general decrease in conductance change as area increases is evident. This is corroborated when only considering devices with channel area larger than 1 mm2  is performed for more than ten million cycles see Fig. . No deviamp Fig. , the per47. In this work, we report on long retention times exceeding 400 seconds (s), shown in Fig. 49; however, when working with biologically relevant systems, the average frequency of events is <1 Hz. Hence, 400 s retention time is adequate. Additionally, due to the dependence of Nafion ion conductivity on hydration, there is a possibility to tune the retention by controlling the moisture level in the Nafion membrane50. The dominance of short-term potentiation depends on the conductance regime of the device, where changes in conductance are primarily short-term when the conductance is near maximum and minimum , and to then transfer the weights to memory with long retention time for inference when training is finished55. As a result, the targeted application of the BLASTs is for neural network training.Retention time is another essential figure of merit for synaptic transistors and many neuromorphic computation systemssee Fig. , the dev2 to ~100 mm2 were fabricated and tested by applying a set current pulse of 1 µA amplitude, 10 ms duration and recording the change in conductance. The calculation of charge modulation through current spike and energy per spike calculation are given in Supplementary Note 2 in energy dissipation, but the results still indicate that device scaling still greatly benefits the devices. These results still exceed or match comparable organic and 2D devices at a competitive speed  graphs presented in Fig. 47, we show that such asymmetry of experimental data can yield algorithmic benefits in compute-limited situations.Although the aforementioned mBLASTs are biocompatible, feature superior energy efficiency, and have outstanding neuromorphic plasticity characteristics, the devices are still rather large. In order to implement more complex neuromorphic functionalities, the device sizes must be scaled down, which we achieved by building microscale devices (µBLASTs). The µBLASTs were made using UV-lithography with HD-8820 passivated graphene field-effect transistors  University of California-Irvine Human Activity Recognition (UCI-HAR)59 dataset for movement classification from biometric sensor data, and (3) Fashion-MNIST60 clothing article classification. The mBLAST devices were applied on MNIST and UCI-HAR, while the performance of the µBLASTs was evaluated on UCI-HAR and the more complex Fashion-MNIST datasets. Training is accomplished by simulating a small multilayer perceptron with two synaptic weight layers L1 and L2 using CrossSim61, a physics-rich neuromorphic crossbar simulator. See Methods for more details on the training simulation setup.After analyzing the device characteristics of the mBLASTs and µBLASTs, the experimental conductance update characteristics are used to simulate online learning neuromorphic tasks. The experimental device data is used to construct LUTs that can be directly sampled by the crossbar simulator, allowing the simulation to account for linearity, symmetry, and write noise directly from the experimental results. The following three classification tasks were chosen: (1) Modified National Institute of Standards and Technology (MNIST)62 updates  and one trained using µBLASTs. The representative distributions for the two neural network layers are plotted in in Fig. 2) at >10 kHz speeds, competitive with other 2D materials-based devices and allowing the potential for the interfacing of biological and artificial synapses. Neural network simulations show that the low read and write noise, coupled with the linear and symmetric synaptic characteristics of the mBLASTs allow for near-ideal classification. More importantly, the µBLASTs are demonstrated to be metaplastic synapses that realize weight normalization, a weight regularization algorithm used to train generalizable neural networks. This allows the performance of µBLAST networks to exceed the classification accuracy achieved by ideal linear numeric weights on difficult tasks. These characteristics make BLAST devices promising candidates in the intersection of bioelectronics and neuromorphic computing.In conclusion, we have demonstrated a novel solid electrolyte gated graphene device with unique artificial synapse behavior. We experimentally revealed that these bio-compatible devices operate at low energy density (<50 aJ/µm2 square was then cut out and spin-coated with PMMA (PMMA 950 A4) to yield a ~200 nm thick layer. To do so, the copper/graphene stack must be placed on a silicon wafer with the graphene facing up. Kapton tape is then used to secure all sides of the copper/graphene stack to the wafer such that a watertight seal is formed under the copper to prevent PMMA from leaking underneath the stack. Afterwards, PMMA (950 A4) is spin-coated onto the stack at ~2500 rpm for 60 s. The sample is then baked on a hotplate at 200 °C for 15–20 min before it is ready for etching.The graphene electronic tattoos were fabricated starting with monolayer CVD grown large-scale graphene on copper, purchased from Grolltex. A~2 × 2 in4)2S2O8, 0.1 M) to etch away the copper. The PMMA/graphene film is then cleaned in a series of water washing steps and then transferred onto a temporary tattoo paper. The graphene/PMMA/tattoo paper is then dried and cut into arbitrary desired locations. In order to transfer the graphene/PMMA onto an arbitrary surface, it is first soaked in water for ~5–10 min.The PMMA/graphene/Cu foil is then placed into ammonium persulfate  film. After preparing the gold EVA gate, it is brought in contact with a piece of Nafion-117 of the desired dimension . This step is performed on a hotplate (at ~150 °C) for no longer than 15 s. Following the application of the Nafion strip, adhesive gold contacts are placed 3–10 mm apart from each other, perpendicularly from the gate, on top of the Nafion to form the source and drain terminals. The GETs (~5–15 cm length and 3–10 mm width) are prepared for transfer onto the BLAST device starting by soaking in DI water for 5–10 min. The graphene should show signs of slight separation from the tattoo paper when sufficiently soaked. The GET can then be placed on top of the Nafion and transferred onto the top of the device such that the graphene contacts both gold source-drain contacts and forms a channel across the Nafion.CVD grown graphene was covered with a 200 nm thin layer of PMMA for transfer. After etching copper in ammonium persulfate (see above), the graphene was transferred onto the wafer with pre-fabricated Au/Ti (10/50 nm thick) markers. A photoresist was used to protect the graphene channel areas during exposure to oxygen plasma. The stack of 10 nm Ti and 90 nm Au was e-beam assisted evaporated on the wafer through a pre-defined structure of lift-off resists to form source and drain electrical connections. Photostructurable polyimide, HD8820 was used in the last step to form the passivation. Spin- coated at 5000 rpm, exposed at i-line UV light, developed in 0.26% TMAH, and hard baked at 350 °C, the polyimide forms a 3 µm thick passivation. The devices were then diced and spin-coated with the liquid Nafion-117 containing solution (Sigma–Aldrich) three times , forming a 666.7 ± 28.9 µm thick layer.The three-terminal devices were measured using high-precision source/measure unit Agilent 2902B. One of the SMUs is used to apply 0.1 V of the drain-source potential, while the gate is used in the current-pulsing mode to apply conductance changes.Two pulse train tests were used to characterize conductance change due to periodic pulsing. This was done by sending either 50 or 250 consecutive write current pulses with corresponding widths and amplitudes of 100 µs and 100 µA, 1 ms and 10 µA, 10 ms and 1 µA, and 100 ms and 100 nA. All pulse trains were conducted with both positive and negative pulses. The pulses were followed with a 150–200 s relax to observe conductance retention.A pulse ramp experiment ran multiple sequences of a train of 20 negative pulses followed by a train of 20 positive pulses. The level experiment consisted of an arbitrary sequence of negative and positive current pulses of 10, 20, and 30 µA that were pulsed through the gate every 5 s at a pulse width of 1 ms. Both tests were used to demonstrate distinct and repeatable conductance levels.The temperature dependence test measured macroscale device performance at varying temperatures . The 0° test was performed inside of an insulated Styrofoam container filled with ice. The macro device was lowered onto ice using a petri dish and connected to the Keysight B2902A using wires thin enough as to not disturb the insulating seal. At 25°, 40°, 60°, and 80°, the device was secured to the top of a hotplate using Kapton tape while a thermocouple was used to verify the temperature. The above-mentioned tests were performed at each temperature with a minimum time of 30 min after each temperature change to ensure the device was successfully brought to temperature.61. During a weight update, the ideal backpropagation update is first calculated. To account for synaptic device nonlinearity and write noise, the probabilistic LUT of device updates is queried to sample a conductance change ΔG that depends on the current value of the conductance G of a synapse device. To model device-to-device process variations, each device in the simulated array is randomly assigned to one of four (three) LUTs, each of which is constructed from a different experimentally characterized μBLAST (mBLAST) device. To represent both positive and negative weights, a single weight value is encoded in the difference in conductance between two paired BLAST devices. This is done for two reasons: (1) computing the difference in analog reduces the required dynamic range of the peripheral circuits, (2) using two devices per weight, rather than a fixed analog offset for all devices, avoids coupling the variability and noise in the offset resistor into all the results, which effectively amplifies the variability. When a weight is updated, the conductance of both devices in the pair are updated. The weights are trained using stochastic gradient descent with a batch size of 1, i.e. training samples were shown one at a time. A two-layer MLP was trained for each task, where a sigmoid activation is used after the first layer and a softmax is used after the second layer. The MNIST and Fashion-MNIST networks use a 785 × 300 weight matrix for the first layer and a 301 × 10 weight matrix for the second layer (including bias). The UCI-HAR MLP has a 577 × 200 matrix and a 201 × 6 matrix for the first and second layer, respectively.Neural network training simulation results and synaptic update lookup tables were generated from experimental data using CrossSimSupplementary Information is available and includes comparison table; Supplementary Information