Fifteen healthy adult smokers participated in the two-part study to assess the distribution and accumulation of [11C]nicotine in the respiratory pathways and brain. Time-activity data for the respiratory pathways, lungs, oesophagus and brain were derived. 31–36% of both inhaled tracer formulations accumulated in the lung within 15–35 s. [11C]Nicotinefreebase exhibited higher uptake and deposition in the upper respiratory pathways. For [11C]nicotinelactate, brain deposition peaked at 4–5%, with an earlier peak and a steeper decline. A different kinetic profile was obtained for [11C]nicotinelactate with lower tracer uptake and accumulation in the upper respiratory pathways and an earlier peak and a steeper decline in lung and brain. Using nicotine lactate formulations in e-cigarettes may thus contribute to greater adult smoker acceptance and satisfaction compared to freebase formulations, potentially aiding a transition from combustible cigarettes and an acceleration of tobacco harm reduction initiatives.Smoking is a cause of serious disease in smokers. Electronic cigarettes, delivering aerosolized nicotine, offer adult smokers a potentially less harmful alternative to combustible cigarettes. This explorative PET/CT study investigated the distribution and deposition of inhaled [ Smoking tobacco is a cause of serious diseases in smokers, including lung cancer, heart disease and emphysema. The best action adult smokers can take to reduce these risks is to stop smoking or using nicotine products completely. However, for adult smokers who are neither interested nor willing to quit smoking, a growing number of regulators and public health bodies posit that encouraging them to transition to using non-combustible nicotine products, that are substantially less harmful than inhaled tobacco smoke, is the next best option ,2. This E-cigarettes are battery-powered devices that deliver aerosolised nicotine (if included in the formulation), propylene glycol and/or glycerol, and may contain flavorings from an e-liquid of known chemical composition. They do not contain tobacco nor require combustion and are available in two principal configurations: “open” systems, which can be refilled by the consumer  or “closed” systems . When the user takes a puff on the product or activates via a push button, a heating element is activated, converting the e-liquid into an aerosol that the user inhales. A recent Cochrane Review concluded that (1) e-cigarettes help adult smokers to stop smoking (even among those who do not intend to quit smoking) and are more effective than traditional nicotine replacement therapies (NRT) or willpower alone, and (2) and are not associated with serious unwanted effects or harm with up to 2 years of usage . As e-ciAdult smokers are more likely to transition to less harmful products such as e-cigarettes if the product provides a satisfying experience. Thus, to maximize the public health benefits of e-cigarettes, it is important that e-cigarettes provide adult smokers with a similar experience to combustible tobacco smoking, but in a less harmful way . Traditimyblu™ e-cigarette device, there was also a trend of dose proportionality for the pharmacokinetic parameters measured. All three pharmacokinetic endpoints thus indicated a more effective absorption profile for nicotine with the nicotine lactate formulation, and corresponding adult smoker satisfaction scores, than the freebase nicotine formulation with the same e-cigarette device under a controlled puffing regime.Freebase nicotine is more volatile than nicotine salt . As a re11C]nicotine, was designed to investigate the above absorption hypothesis, i.e., whether nicotine lactate has a more effective uptake than a nicotine freebase formulation. This PET imaging approach was used to explore the [11C]nicotine deposition and disposition in the oral cavity, lungs, and brain to better understand the principal absorption route and kinetics of nicotine in vivo following aerosol inhalation from the myblu™ e-cigarette device.To complement this previously published pharmacokinetic study, and to gain a further mechanistic understanding of nicotine lactate deposition, an explorative, proof of concept, PET-study, using [11C]nicotinefreebase and [11C]nicotinelactate in summation images comprising the first image frame where the e-cigarette was removed from the field of view until 10 min after [11C]nicotine administration. The freebase formulation visually showed substantial accumulation in the oral cavity and upper respiratory pathways  and [11C]nicotinefreebase, the highest SUV was found in the oral cavity , followed by the oesophagus mouth (27.8 ± 15.4)), the trachea mouth (19.1 ± 3.65), and the upper lung region (6.88 ± 3.82). The peak SUV was reached at 25 s for the upper lung and at 110 s and 165 s for the trachea mouth and for the oral cavity, respectively. By contrast, the TACs for the oesophagus mouth peaked at 1650 s (27.5 min.),For [11C]nicotinelactate were substantially lower than [11C]nicotinefreebase for the oral cavity , oesophagus mouth  and trachea mouth (3.99 ± 5.41 79% *). An exception was the upper lung region that had a slightly higher SUV  for [11C]nicotinelactate. The TACs demonstrated a peak SUV at 15 s for the upper lung, 70 s for the trachea mouth, 90 s for the oral cavity, and 210 s for the oesophagus mouth. , 3.52 ± 2.38% in the upper lung (at 25 s), 0.78 ± 0.19% trachea mouth (at 110 s), and 0.31 ± 0.12% oesophagus mouth (at 165 s). For the [11C]nicotinelactate the peak depositions were 5.49 ± 1.33%, in the upper lung (at 15 s), 1.52 ± 1.39% in the oral cavity (at 90 s), 0.14 ± 0.17% in the trachea mouth (at 70 s) and 0.11 ± 0.12 in the oesophagus mouth (at 210 s). The peak depositions for [11C]nicotinelactate were lower for the oral cavity 84% **, mouth trachea 82% ** and mouth oesophagus 66% but higher for the upper lung region 56%. , followed by the oesophagus lung (20.3 ± 9.47) and the trachea lung (17.8 ± 4.99) and the average SUV for lung and deep lung peaked at 8.38 ± 0.86 and 6.95 ± 2.34, respectively. Most regions reached their maximum SUV between 25 and 70 s after inhalation. However, the average TAC for the oesophagus lung reached a peak at 27.5 min.11C]nicotinelactate, the average peak SUVs were similar in the primary-secondary bronchi, , deep lung (8.38 ± 8.42), and lung (7.95 ± 4.39), i.e., all about 8), and lower for the oesophagus lung (4.06 ± 1.51 80% lower* than freebase SUV) and the trachea lung .  peaked at 15 s after inhalation.For [11C]nicotine freebase and lactate formulations in the lung were 35.5 ± 9.12 and 31.0 ± 9.62%, respectively (p = 0.41). In the other regions , the [11C]nicotine deposition was low, 0.10–1.98% for [11C]nicotinefreebase and 0.10–0.31% for [11C]nicotinelactate. The deposition was highest at 35–70 s after inhalation for [11C]nicotinefreebase and at 15–25 s for [11C] nicotinelactate.  lung through inhalation of the e-cigarette aerosol.For the freebase formulation, the SUV values and the accumulation were higher in the oral cavity, respiratory pathways and the oesophagus. This deposition pattern is consistent with two modes of inhalation of nicotinelactate and 35 s for [11C]nicotinefreebase. Furthermore, the nicotine lactate formulation passed the respiratory pathways faster than freebase, most likely due to less surface deposition than the freebase formulation. [11C]Nicotinelactate also had a faster clearance from the lungs, which may indicate that the lactate formulation has a quicker and more complete redistribution into the circulating blood than the freebase formulation. This is along the line with the pharmacokinetic data reported by O’Connell et al. nicotine was in the brain at 4.5 and 12.5 min after the inhalation of the lactate and freebase [11C]nicotine formulation, respectively, indicating slower kinetics for [11C]nicotinefreebase. Mukhin et al. [11C]nicotine enter and accumulate in the brain tissue via systemic blood circulation.The data shows that 4–5% of the inhaled [n et al.  reported11C]nicotine kinetics in arterial blood was observed with discrete arterial sampling over 30 min. The blood absorption was fast for both formulations, with the highest value observable after two min, decreasing quickly to a phase with slow elimination. No differences were discernible between the two formulations, possibly due to the data collecting timepoints chosen because of the technical limitations of the blood sampling method. However, the peak, irrespective of formulation, was probably earlier than two minutes as a study comparing e-cigarettes and combustible cigarettes reported that brain [11C]nicotine concentrations rose quickly (23 ± 3 s) after a single puff of the e-cigarette, as did another study where [11C]nicotine was administered in a combustible cigarette nicotine solutions varied between 6–8; this is most likely due to a low buffering capacity of the added reference formulations. A regional difference in distribution and deposition was still observable for the two [11C]nicotine formulations as the data presented in the current study showed [11C]nicotine in the freebase formulation deposited in the mouth and upper respiratory tract and [11C]nicotine in the nicotine lactate formulation deposited in the alveoli. This result is in line with the hypothesis that freebase nicotine is indeed more volatile and thus more likely to off-gas from the aerosol droplets than the less volatile nicotine salt resulting in a different nicotine deposition profile [11C]nicotine via e-cigarette aerosols is sufficient for the evaluation of nicotine distribution to the lungs, circulating blood, and brain.The two formulations used in the present study had an identical chemical composition apart from the charged state of nicotine, rendering the pH values to be 3.98 for the lactate formulation and 9.98 for the freebase formulation see . The pH  profile ,19,27. MThis was a small, descriptive and explorative study conducted in a laboratory setting, that was not designed to fully evaluate freebase and nicotine salt deposition in lung and oral tissues. As such, the small number of study subjects does not allow for comprehensive statistical comparisons to be conducted. This may explain differences in individual results based on variations in e-cigarette inhalation technique and anatomy between the subjects.S)-Nornicotine bitartrate was bought from Pharma Synth . (S)-Nornicotine was obtained as freebase via basification of an aqueous solution and extraction by diethyl ether according to an established in-house method. Solutions of nicotine and nicotine lactate salt in glycerol and propylene glycol were supplied by the study sponsor. Other reagents and solvents were obtained from Sigma-Aldrich , Fresenius-Kabi, , Merck  and VWR  and used without further purification. The synthesis was automated using Synthia, an in-house built synthesis equipment.(w/w]). Furthermore, the formulations were optimized to ensure that the freebase nicotine formulation contained a minimal amount of protonated nicotine and the nicotine lactate formulation contained a minimal amount of freebase nicotine. Both formulations were also unflavored to ensure no flavoring compounds could influence nicotine protonation and hence possibly the deposition or absorption -nicotine ([11C]nicotine) was synthesized by methylation of 1–2 mg (S)-nornicotine with [11C]methyl iodide [11C]Nicotine was purified with semi-preparative HPLC, using an Agilent Infinity 1260 II system equipped with a C18  column using 50 mM ammonium formate pH 3.5 and acetonitrile as the mobile phase with UV detection at 254 nm and a Bioscan flowcount radiodetector. [11C]Nicotine was extracted from the collected fraction by adjusting pH > 10 by the addition of a 1 M sodium carbonate solution and retained on a tC18 (plus) SPE-column, which was washed with water. The SPE-column was flushed with 30 mL of air using a syringe. [11C]Nicotine was then eluted with diethyl ether, dried by passage through potassium carbonate SPE-column, into a septum equipped glass vial containing 20 µL of reference formulation . The solution was concentrated to about 20 µL under a stream of nitrogen and heated at 50 °C. 10 µL of sterile water and 220–300 µL of reference formulation (the exact volume depended on the radioactivity) were added to the [11C]nicotine-solution giving a final formulation of [11C]nicotine in 2.5% nicotine as freebase or nicotine lactate in glycerol and propylene glycol without flavoring. A myblu™ e-cigarette was specially designed with a piece of cotton wool to absorb the radioactive [11C]nicotine solution. Then 100 µL of the formulated [11C]nicotine was applied to the cotton wool, and the myblu™ e-cigarette was reassembled. For each experiment, two myblu™ devices were prepared. Determination of identity and radiochemical purity was performed by analytical reversed-phase HPLC on an Agilent 1290 Infinity II system with eluents 8.1 mM ammonium carbonate and acetonitrile and equipped with a C18 (Phenomenex Gemini NX C18 5 µm (4.6 × 100 mm)) column using UV detection at 254 nm and a Bioscan flowcount radiodetector. The radiochemical purity was >98% for all [11C]nicotine productions. were aerosolized and inhaled using the mybluTM e-cigarette. Each study subject inhaled one of the formulations. Hereafter, the mybluTM e-cigarette is referred to as the e-cigarette. In part A, the study subjects performed two 40 min [11C]nicotine PET/CT scans, one over the lungs and one over the mouth and upper respiratory pathways. The time between PET/CT sessions was at least three hours to allow for decay and washout of [11C]nicotine. In part B, subjects had one 30 min [11C]nicotine PET/CT scan over the brain. [11C]Nicotine was administered as 1–2 puffs, inhaled as deep draws from the e-cigarette. The subjects in part B of the study underwent a 3D T1-weighted (T1w) brain volume sequence for anatomical reference on the same day as the PET/CT scan. In part A, the ultra-low dose CT (ULDCT) scan for attenuation correction initiated before the PET scan was also used for anatomical information.This study was an exploratory PET-study in two parts. Part A was designed to elucidate the uptake and deposition of inhaled Fifteen healthy adult subjects , 51–65 (58 ± 4.4 years old) with a body weight between 51 and 85 kg (average 71 kg), were enrolled in this study , oral cavity , or brain (B). The PET scans were performed on a digital Discovery MI PET/computed tomography (CT) system . The scanner enables the acquisition of 89 contiguous image planes (slices) with a 25 cm axial field of view, which allows imaging of the entire lung. The first two subjects had their scans on a 20 cm axial field of view scanner, prior to the upgrade to 25 cm.11C]nicotine (MBq) was inhaled and a PET acquisition , totaling 30 and 40 min for part B and A, respectively, was started simultaneously with the [11C]nicotine inhalation. Images were reconstructed to a 256 × 256 pixel matrix with a field of view 50 and 25 cm for lung and oral cavity or brain, respectively, using time-of-flight ordered subsets expectation maximization with 3 iterations and 16 subsets, including resolution recovery and a 4-mm Gaussian post-processing filter. The time of [11C]nicotine inhalation and inhaled amount of radioactivity were registered. The inhaled amount of radioactivity was corrected for activity remaining in the e-cigarette device after inhalation.First, an ultra low dose CT attenuation correction scan  was obtained, whereupon Nicotine was formulated with either 2.5% freebase nicotine ([11C]nicotinefreebase) or nicotine lactate ([11C]nicotinelactate). The e-cigarettes, filled with 100 µL of the specific formulation spiked with [11C]nicotine, were delivered just a few min before each PET/CT-scan session. [11C]Nicotine was administered as one to two puffs on the e-cigarette followed by inhalation of the aerosol into the lungs at the onset of the dynamic [11C]nicotine PET scan. For the freebase formulation, 20.0 ± 4.8 MBq (n = 10) and 27.2 ± 2.6 MBq (n = 3) was inhaled for the mouth and lung scan and for the brain scan, respectively. For the nicotine lactate formulations, the doses inhaled were 24.6 ± 5.3 MBq (n = 10) and 35.0 MBq (n = 1) for the mouth and lung scan and for the brain scan, respectively. Use of the e-cigarette device was practiced with e-cigarettes containing the same formulation used in the study but with unlabelled nicotine  on the screening day to familiarize participants with both the inhalation technique and the aerosol generated by the e-cigarette. Inhalation was repeated with empty dummy devices (nicotine free) in the morning before the first PET session.nicotine inhalation until the end of the PET session. Consumption of alcohol was not allowed within 48 h prior to the first PET session or during the clinic stay. A breakfast was served before the first PET session and lunch before the second PET session.Smoking or use of nicotine, except the 11C]nicotine inhalation. The serial discrete arterial blood samples were analysed using an in-house developed well counter detector system cross-calibrated with the PET/CT scanner. Sample weight, number of counts and measurement time were recorded and the radioactivity concentrations, corrected for physical decay to the start of [11C]nicotine inhalation, were calculated.Arterial blood samples, approximately 2 mL, were collected at 2, 4, 6, 8, 10, 15, 20, and 30 min after nicotine PET summation images. The delineated VOIs were subsequently transferred to the corresponding individual dynamic [11C]nicotine PET scan for the generation of time-activity curves (TACs). Hermes software , was used for VOI delineation and extraction of time-activity data. An example of the resulting VOIs is shown in The lung, deep lung, and trachea were delineated using the anatomical information from the ULDCT scan. Primary, secondary bronchi, oral cavity, and oesophagus were outlined on a combination of the individual’s ULDCT and [11C]nicotine PET image. The T1w MR images were segmented into grey and white matter. Both segmentation and co-registration were conducted using SPM8 . Subsequently, whole-brain (grey and white matter) VOIs were defined on the segmented and co-registered structural T1w images utilizing an automated probabilistic template as implemented in the PVElab software [11C]nicotine images.For analysis of brain scans (part B), the PET summation image was used for a co-registration of the T1w MR volume to the summed [software . TACs we11C]nicotine deposition, defined as the fraction of radioactivity measured in a delineated VOI related to the inhaled radioactivity, was calculated.TACs were converted to SUV curves by normalization to injected dose per kilogram body weight. Furthermore, the [11C]nicotine was fast and reached the peak value in the lungs after approximately 15 s (nicotine lactate) compared to 35 s (freebase) after [11C]nicotine aerosol inhalation using the e-cigarette device. Moreover, approximately 31–36% of the inhaled [11C]nicotine was disposed in the lung within the first 15–35 s after [11C]nicotine inhalation from the e-cigarette. [11C]nicotinelactate passed the upper respiratory pathways to a greater extent than [11C]nicotinefreebase, which showed a higher accumulation in the upper respiratory pathways, in line with previously reported blood nicotine delivery data.The exploratory study presented herein showed that the distribution of [11C]nicotine levels in arterial blood indicated an entry and accumulation of [11C]nicotine in the brain tissue via systemic blood circulation. Approximately 4–5% of the inhaled [11C]nicotine was distributed to the brain, where the nicotine lactate formulation showed an earlier peak and elimination compared to the freebase formulation. Although further studies are warranted as this study was based on a small number of subjects, this observation may explain greater adult smoker satisfaction and reductions in a desire to smoke combustible cigarettes with nicotine salt formulations compared to freebase in other e-cigarette studies [Furthermore, the [ studies ,21,22,23


Frontiers in Aging Neuroscience Topic contains a series of high-quality original articles and updated reviews covering recent 2-year progression in various aspects of α-Syn expressions in blood, saliva, and cerebrospinal fluid (CSF), and pathological processes in regulating human neurodegenerative disorders as well as its feasibility as an early predictive biomarker. Parkinson's disease (PD) is a typical disease in α-synucleinopathies, presenting dominantly with clinical symptoms of motor impairment. PD is characterized by progressive chronic neurodegeneration and loss of dopaminergic (DA) neurons in brain, mainly substantia nigra. Meanwhile, dementia with Lewy bodies (DLB), and multiple system atrophy (MSA) belong to this devastating neurodegenerative category. Currently, PD cannot be completely cured or reversed, and existing treatment strategies have limited efficacy in the early stages. Therefore, there is an imperative need to clarify the pathogenesis of PD and find early identifications of it. However, the detailed etiology and pathogenesis remain no consensus. This Research Topic aims to generate a multidimensional discussion linking clinical analysis with basic experiment unveiling the fundamental mechanisms of PD. Then filtrate the current effective biomarkers for preclinical detection and clinical evaluation on this basis. As a result, building a predictive model contributes to a deeper understanding of the mechanism of PD, so as to better guide clinical applications.The recognition of Alpha-Synuclein (α-Syn) as an essential player in the pathobiology of human neurodegenerative diseases has spurred substantial efforts to understand its biochemical and pathological function. This Du et al. summarizes useful biomarkers for the diagnosis of PD, as well as how the biomarkers α-Syn could potentially be used as a biomarker for PD step by step from genetics, immunology, fluid and tissue, imaging, as well as neurophysiology. As a general overview, Ganguly et al. also evaluate the overall effectiveness of α-Syn in forecasting PD progression. In view of the overwhelming importance of α-Syn in the pathogenesis, a detailed analysis is then made of various studies to establish the biomarker potential of this protein in PD. Knowledge of the α-Syn-related novel biomarkers for preclinical detection and clinical evaluation serve to identify the disease-predictive biomarkers more effectively, which are critical for precise diagnosis and therapeutic approaches.Various attempts made by the researchers to develop preclinical and clinical investigations encompassing genetics, immunology, microbiology, neuroimaging, and neurophysiology to clarify the molecular mechanisms and identify useful biomarkers for the disease diagnosis. There are two valuable systematic reviews on this topic, which comprehensively describe the current research status. In the first place, Li et al. in this case by studying the role of neuroimaging in the diagnosis of MSA-P. Using a novel magnetic resonance technology, amide proton transfer (APT) imaging, which is a well-studied chemical exchange saturation transfer imaging for its sensitivity to mobile protons and peptides in tissues. Next, Xian et al. demonstrate the diagnostic value of co-registration analysis of 18F-flurodeoxyglucose (18F-FDG) and 18F-Dopa positron emission tomography (PET) for differentiating MSA-P from PD. Segmentation analysis of the rabbit-shaped appearance of the striatum segment of PET images not only could distinguish PD and MSA-P patients from healthy controls (HCs).Early warning biomarkers have become a hot topic recently due to the need for diagnosis and treatment in forepart. Inchoate detection and differential diagnosis of parkinsonism-type MSA (MSA-P) and PD are in great clinical need, as aging can lead to many diseases that are difficult to detect at early stage. This urgent issue is also addressed by Si et al. assess the clinical significance of magnetic resonance imaging (MRI)-visible enlarged perivascular space (EPVS) in patients with idiopathic RBD (iRBD) and PD. Their study indicates that iRBD patients had significantly greater EPVS burdens than PD patients. Specifically, higher EPVS burdens in centrum semiovale (CSO) and basal ganglia might be independent risk factors for iRBD, and the higher the value the more severe the clinical symptoms. Then, Chen et al. combine clinical assessments, dopamine transporter (DAT) imaging data, CSF and genetic data of PD patients to minister to develop a prognostic model for predicting mild cognitive impairment (MCI) in PD-RBD patients. As CI is one of the most common non-motor symptoms in PD, incidence of CI in PD patients increases as the disease progresses, thus investigating risk factors and predictive model for CI may contribute to a better understanding of the PD-RBD phenotype and help decelerate the MCI process in PD-RBD patients in clinical practice.Rapid eye movement sleep behavior disorder (RBD) is marked by repeated nocturnal sleep-related normal skeletal muscle atonia with remarkable motor disturbances and dream enactment behaviors, and the existence of RBD can be considered as an omnipotent precursor to PD. Therefore, scholars have started with exploring the interconnected mechanisms between RBD and PD and the course progression to identify early predictive indicators of PD. The article by Lian et al. tackles this issue and suggests that neuroinflammatory factors in CSF, especially tumor necrosis factor (TNF)-α, may cause damage to dopamine (DA) neurons and induce the depletion of DA, which is related to the occurrence and development in PD patients who accompanied with depression. Also, adaptive immune system, including lymphocyte subpopulations and cytokines, is involved in the PD pathogenesis. The article by Huang et al. analyzes integrated substantia nigra (SN) and gene expression by CIBERSPOT and identifies the function of key immune cell types in PD. They propose the immune response modifying the genetic background as a factor influencing the synaptic pathway in PD pathology. Another popular inflammation topic normally discussed in pathogenesis of neurodegenerative disease is gut-brain axis. The gut-brain axis is a two-way regulating axis for the interaction between gastrointestinal function and the CNS, and the nerve-endocrine-immune network is an important connection method. Following this clue, Bu, Huang, et al. assess α-Syn immune-neuron reactivity and expression patterns. Contrary to our expectations, they propose that gastrointestinal pathology couldn't be facilitated for diagnosing α-synucleinopathies, including PD. The deposit of α-Syn is age-dependent rather than neurodegenerative-unique in the enteric nervous system (ENS) of patients.The central nervous system (CNS) is so sophisticated and fragile that is vulnerable to injury, pathogen, or neurodegeneration. In the process of aging and degradation, the activation of the innate immune system occurs. The inflammasome is a signal complex that regulates immune cells, chiefly microglial, leading to the release of pro-inflammatory factors. Consequently, pro-inflammatory factors aggravate the neuro-inflammatory response, neurological degeneration, and eventually upset the brain metabolic balance. It can be deduced that analyzing the inflammatory factors in CSF is more meaningful to investigate brain neuroinflammation. For instance, studies have shown that immune dysfunction may lead to aberrant α-Syn aggregation and transmission in the brain. The article by SNCA) is the first identified pathogenic gene that causes autosomal dominant PD, and its abnormal protein aggregation is regarded as the main pathological symbol of patients. Based on this understanding, Guo et al. evaluates the associations between the SNCA gene variants and PD through conventional whole-exome sequencing (WES) and sanger sequencing. Beyond expectations, the result shows that variants located in the coding regions of the SNCA gene may have less or no role in the development of PD in the Han Chinese population, which implies the inescapable role of ethnic differences in PD development. Therefore, in order to verify the function of SNCA, it is necessary to carry out cohort comparisons with large samples of multiple races in the future. Neuro regulation is the result of multi-gene collaboration. Both α-Syn and Monoamine oxidase A (MAOA) are long deemed as the pivotal pathogenesis of PD, Jia et al. further explores the close crosstalk between these two co-regulation molecules. At the same time, Bu, Xie et al. reveal that overexpression of long non-coding RNA (lncRNA)-T199678 mitigates the α-Syn-induced dopaminergic neuron injury, including oxidative stress, abnormal cell cycle, and apoptosis, which contributed to promote PD. This study praises a new molecular target for further genetic pathway investigation in PD.Apart from aging, genetic dysfunction is widely considered to be a key risk factor of PD, and is repeatedly discussed when exploring the etiology and pathology of α-Syn. So far, at least 23 genes and independent risk signals related to PD have been discovered. Among them, it is indisputable that the α-synuclein gene  or osteopontin (OPN) could be candidate biomarkers in differentiating PD patients from HCs. Besides, inflammation may be involved in the interaction between these two bone-derived factors and PD, which partly explains the various modulating effects of bone physiological processes on the CNS.Multiple system diseases often occur in the elderly. It is often found that patients with neurodegenerative diseases are complicated by osteoporosis, and fractures are more likely to be found in the prodromal phase of PD in clinical practice. As the main supporting and protecting organs of our body, bone is also an active immune and endocrine organ. It can secrete a variety of bone-derived factors, interact with the brain through various bone-derived cells, and participate in the pathophysiology of various diseases. First, the article by Shim et al. supporting the occurrence of cholinergic dysfunction on motor deficits in PD. The mechanisms of progressive loss of DA neurons in PD include a variety of cellular processes such as α-Syn aggregation, mitochondrial damage, oxidative stress, calcium homeostasis dysfunction, interruption of axonal transport, and neuroinflammatory damage. These abnormal phenomena can disturb the balance of protein homeostasis in PD. In a review by Ren et al., the cross-linking mechanisms of endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) and the dysregulated autophagy in DA neurodegeneration are systematically addressed.New methods and molecules are gradually being tried for scientific research. Recently, exosomal biomarkers have been involved in embedding the acknowledgment and diagnosis of neurodegenerative disorders. Exosomes are small extracellular vesicles coming from different cell types and can contain different molecular components such as proteins, lipids, and RNA. Previous reports have demonstrated increased exosomal α-Syn in patients with PD in comparison to HCs. In this context, the exosomal acetylcholinesterase (AChE) and α-Syn are quantified and the inner relationships with clinical symptom are simultaneously scouted by Wang Z. et al. target at the amplitude of low-frequency fluctuations (ALFF) and discover that PD patients exhibited different tendencies in several brain regions, emphasizing the diversity of widespread abnormal brain activities related to PD, and suggesting that the abnormalities of ALFF in specific frequency bands would be helpful in detecting the neural changes in PD. Expect for traditional neuroimaging, nowadays researchers use more controllable electrophysiological tools, such as TMS, to reveal the conduction blocks caused by structural abnormalities or neurodegenerative diseases. The article by Xu et al. applies a more sensitive and accurate method, triple stimulation technique (TST), to quantitatively assess the integrity and impairment of corticospinal pathway in PD patients.Neuroimaging is also a traditional technique for detecting the connectivity of brain functions, which is often used in interpreting the static state and dynamic changes of patients in clinical research. Focusing on the brain imaging network in aging, it is wildly acknowledged that PD patients depict abnormal spontaneous neural activity in resting-state functional magnetic resonance imaging (r-fMRI). Wang Q. et al. summarizes recent research on EEG characterization related to the cognitive and motor functions in PD patients and discussed its potential to be used as diagnostic biomarkers. Abnormalities in β and δ frequency bands are, respectively the main manifestation of dyskinesia and cognitive decline in PD. In conclude, the unique time-frequency characteristics of EEG could provide further insight into disrupted sensorimotor networks in PD with motor deficits, such as FOG, dyskinesia, and dystonia in a real-world environment. Several of the contributors to this topic recognized the need to promote cognition of older people. The exploration of specific brain regions for PD cognitive impairment is ongoing. The dorsolateral prefrontal cortex (DLPFC) transmits afferent projections to the caudate nucleus and putamen and participates in advanced cognitive functions, which is recognized as an important area related to CI. Furthermore, Cai et al. compare the posterior middle frontal gyrus-based functional connectivity differences between PD patients with MCI and normal cognitive PD patients. As discussed previously in this editorial, this topic accumulates growing evidence that cognitive dysfunction of PD patients is different from the cognitive function of normal aging people.Present research regards the electroencephalogram (EEG) as a promising method to study real-time brain dynamic changes in patients with PD. Compared with other functional imaging methods, EEG has high temporal resolution, in which specific frequency analysis enables accurately investigating connectivity fluctuations. Firstly, the review by In summary, PD is a neurodegenerative disease caused by the combined action of genes, age, and environment. The underlying pathological mechanism involves multiple circuits such as gene regulation, neuroimmune, biochemical factors, and neurophysiology, which requires further exploration by establishing multi-factor model. On the other hand, biomarkers can help clinicians reduce the chance of misdiagnosis and help them monitor the improvement of disease pathology during anti-Parkinson's disease drug trials. However, the inconsistent research conclusions in the same direction remind us that many methodological issues related to the detection and quantification of α-syn need to be further verified for their feasibility. At the same time, large sample of long-term follow-up studies comparing the motor dysfunction of different neurodegenerative diseases are needed.JL and PL decided on the basic framework of the editorial, and PL wrote the initial version of the manuscript. Also, JL and PX revised the work. All the authors approved of the final edition for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Access to the chemical inventory of an exoplanet requires high-precision observations, often inferred from individual molecular detections with low-resolution space-based5 and high-resolution ground-based8 facilities. Here we report the medium-resolution (R ≈ 600) transmission spectrum of an exoplanet atmosphere between 3 and 5 μm covering several absorption features for the Saturn-mass exoplanet WASP-39b (ref. 9), obtained with the Near Infrared Spectrograph (NIRSpec) G395H grating of JWST. Our observations achieve 1.46 times photon precision, providing an average transit depth uncertainty of 221 ppm per spectroscopic bin, and present minimal impacts from systematic effects. We detect significant absorption from CO2 (28.5σ) and H2O (21.5σ), and identify SO2 as the source of absorption at 4.1 μm (4.8σ). Best-fit atmospheric models range between 3 and 10 times solar metallicity, with sub-solar to solar C/O ratios. These results, including the detection of SO2, underscore the importance of characterizing the chemistry in exoplanet atmospheres and showcase NIRSpec G395H as an excellent mode for time-series observations over this critical wavelength range10.Measuring the abundances of carbon and oxygen in exoplanet atmospheres is considered a crucial avenue for unlocking the formation and evolution of exoplanetary systems 2 and H2O and detection of SO2.The medium-resolution transmission spectrum of the exoplanet WASP-39b, described using observations from the Near Infrared Spectrograph G395H grating aboard JWST, shows significant absorption from CO WASP-39b is a hot , low-density giant planet with an extended atmosphere. Previous spectroscopic observations have shown prominent atmospheric absorption by Na, K and H2O (refs. 15), with tentative evidence of CO2 from infrared photometry4. Atmospheric models fitted to the spectrum have inferred metallicities (amount of heavy elements relative to the host star) from 0.003 to 300 times solar20, which makes it difficult to ascertain the formation pathway of the planet22. The host, WASP-39, is a G8-type star that shows little photometric variability23 and has nearly solar elemental abundance patterns24. The quiet host and extended planetary atmosphere make WASP-39b an ideal exoplanet for transmission spectroscopy25. The transmission spectrum of WASP-39b was observed as part of the JWST Transiting Exoplanet Community Director’s Discretionary Early Release Science (JTEC ERS) Program27 , which uses four instrument configurations to test their capabilities and provide lessons learned for the community.We obtained a single-transit observation of WASP-39b using the NIRSpec28. The tilt event occurred mid-transit, affecting approximately three integrations and resulted in a noticeable step in the flux time series, the size of which is dependent on wavelength  and hence jumps in fluxgth Fig. . The tilR ≈ 600), excluding the detector gap between 3.717–3.823 μm. The light curves show a settling ramp during the first ten integrations (≈631.4 s), with a linear slope across the entire observation for NRS1. We otherwise see no substantial systematic trends and achieve improvements in precision from raw uncorrected to fitted broadband light curves of 1.63 to 1.03 times photon noise for NRS1 and 1.95 to 1.31 times for NRS2. The flux jump caused by the mirror-tilt event could be corrected by detrending against the spectral trace x and y positional shifts, normalizing the light curves or fitting the light curves with a step function  of 1.08–1.20 for our 344-data-point transmission spectrum  can be attributed to CO2 (28.5σ). H2O and CO2 are expected atmospheric constituents for near-solar atmospheric metallicities, with the CO2 abundance increasing nonlinearly with higher metallicity30. The spectral feature at 4.56 μm (3.3σ) is unidentified at present but does not correlate with any obvious detector artefacts and is reproduced by several independent analyses. The absorption feature at 4.1 μm is also not seen in the RCTE model grids. After an exhaustive search for possible opacity sources , described in the corresponding NIRSpec PRISM analysis31, we interpret this feature as SO2 (4.8σ), as it is the best candidate at this wavelength.We compared the weighted-average G395H transmission spectrum to three grids of 1D radiative–convective–thermochemical equilibrium (RCTE) atmosphere models of WASP-39b see , contain2 would have volume mixing ratios (VMRs) of less than 10−10 throughout most of the observable atmosphere in thermochemical equilibrium, coupled photochemistry of H2S and H2O can produce SO2 on giant exoplanets, with the resulting SO2 mixing ratio expected to increase with increasing atmospheric metallicity34. We find that a VMR of approximately 10−6 of SO2 is required to fit the spectral feature at 4.1 μm in the transmission spectrum of WASP-39b, consistent with lower-resolution NIRSpec PRISM observations of this planet31 and previous photochemical modelling of super-solar metallicity giant exoplanets35. Figure χ2/N best-fit model (PICASO 3.0) with VMR = 10−5.6 injected SO2. The inclusion of SO2 in the models results in an improved χ2/N and is detected at 4.8σ  because their near-solar chemical equilibrium abundances could result in a contribution to the spectrum. We see no evidence of CH4 in our spectrum between 3.0 and 3.6 μm (ref. 23), which is indicative of C/O < 1 (ref. 36) and/or photochemical destruction37. With regards to CO, H2S and OCS, we were unable to conclusively confirm their presence with these data. In particular, CO, H2O, OCS and our modelled cloud deck all have overlapping opacity, which creates a pseudo-continuum from 4.6 to 5.1 μm , which has previously been proposed for gas-dominated accretion at wide orbital radii beyond the CO2 ice line at which the gas may be carbon-rich39. Our C/O upper limit, therefore, suggests that WASP-39b may have either formed at smaller orbital radii with gas-dominated accretion or that the accretion of solids enriched the atmosphere of WASP-39b with oxygen-bearing species2. The level of metal enrichment (3–10 times solar) is consistent with similar measurements of Jupiter and Saturn41, potentially suggesting core-accretion formation scenarios42, and is consistent with upper limits from interior-structure modelling43. These NIRSpec G395H transmission spectroscopy observations demonstrate the promise of robustly characterizing the atmospheric properties of exoplanets with JWST unburdened by substantial instrumental systematics, unravelling the nature and origins of exoplanetary systems.We are able to strongly rule out an absolute C/O ≥ 1 scenario (44 (‘rateints’ files) and by means of customized runs of the STScI JWST Calibration Pipeline with user-defined inputs and processes for steps such as the ‘jump detection’ and ‘bias subtraction’ steps.We produced several analyses of stellar spectra from the Stage 1 2D spectral images produced using the default STScI JWST Calibration PipelineEach pipeline starts with the raw ‘uncal’ 2D images that contain group-level products. As we noticed that the default superbias images were of poor quality, we produced two customized runs of the JWST Calibration Pipeline, using either the default bias step or a customized version. The customized step built a pseudo-bias image by computing the median pixel value in the first group across all integrations and then subtracted the new bias image from all groups. We note that the poor quality of the default superbias images affects NRS1 more notably than NRS2, and this method could be revised once a better superbias is available.f noise’ . Our group-level destriping step used a mask of the trace 15σ from the dispersion axis for all groups within an integration, ensuring that a consistent set of pixels is masked within a ramp. The median values of non-masked pixels in each column were then computed and subtracted for each group.Before ramp fitting, both our standard and custom bias step runs of the edited JWST Calibration Pipeline ‘destriped’ the group-level images to remove so-called ‘1/σ (as opposed to the default of 4σ), as it produced the most consistent results at the integration level. In both custom runs of the JWST Calibration Pipeline, all other steps and inputs were left at the default values.The results of our customized runs of the JWST Calibration Pipeline are a set of custom group-level destriped products and custom bias-subtracted group-level destriped products. In both cases, the ramp-jump detection threshold of the JWST Calibration Pipeline was set to 15TDB were extracted from the image headers for use in producing light curves. None of our data-reduction pipelines performed a flat-field correction, as the available flat fields were of poor quality and unexpectedly removed portions of the spectral trace. In general, we found that 1/f noise can be corrected at either the group or integration levels to similar effect; however, correction at the group level with a repeated column-by-column cleaning step at the integration level probably results in cleaner 1D stellar spectra. This was particularly true for NRS2, owing to the limited number of columns in which the unilluminated region on the detector extends both above and below the spectral trace, as shown in Extended Data Fig. For all analyses, wavelength maps from the JWST Calibration Pipeline were used to produce wavelength solutions, verified against stellar absorption lines, for both detectors. The mid-integration times in BJDBelow we detail each of the independent data-reduction pipelines used to produce the time series of stellar spectra from our G395H observations.45) pipeline on our custom group-level destriped products, treating each detector separately. Using the data-quality flags produced by the JWST Calibration Pipeline, we replaced any pixels identified as bad, saturated, dead, hot, low quantum efficiency or no gain value with the median value of surrounding pixels. We also searched each integration for pixels that were spatial outliers from the median of the surrounding 20 pixels in the same row by 6σ (to remove permanently affected ‘bad’ pixels) or outliers from the median of that pixel in the surrounding ten integrations in time by 20σ (to identify high-energy short-term effects such as cosmic rays) and replaced the outliers with the median values. To obtain the trace position and FWHM, we fitted a Gaussian to each column of an integration, finding a median standard deviation of 0.7 pixels. A fourth-order polynomial was fitted through the trace centres and the widths, which were smoothed with a median filter, to obtain a simple aperture region. This region extended 5 times the FWHM of the spectral trace, above and below the centre, corresponding to a median aperture width of 7 pixels. To remove any remaining 1/f and background noise from each integration, we subtracted the median of the unilluminated region in each column by masking all pixels that were 5 pixels away from the aperture. For each integration, the counts in each row and column of the aperture region were summed using an intrapixel extraction, taking the relevant fractional flux of the pixels at the edge of the aperture and cross-correlated to produce x-pixel and y-pixel shifts for detrending . We also measured the vertical shift in pixels of the spectrum from one integration to the other using cross-correlation and the average PSF width at each integration, obtained by adding all columns together and fitting a Gaussian to the profile to estimate its width. This reduction is henceforth denoted as Eureka! [V1].The first Eureka! reduction used our custom group-level destriped products and applied Stages 2 and 3 of Eureka! Stage 2, a wrapper of the JWST Calibration Pipeline, followed the default settings up to the flat fielding and photometric calibration steps, which were both skipped. Stage 3 of Eureka! was then used to perform the background subtraction and extraction of the 1D stellar spectra. We started by correcting for the curvature of NIRSpec G395H spectra by shifting the detector columns by whole pixels, to bring the peak of the distribution of the counts in each column to the centre of our subarray. To ensure that this curvature correction was smooth, we computed the shifts in each column for each integration from the median integration frame in each segment and applied a running median to the shifts obtained for each column. The pixel shifts were applied with periodic boundary conditions, such that pixels shifted upwards from the top of the subarray appeared at the bottom after the correction, ensuring no pixels were lost. We applied a column-by-column background subtraction by fitting and subtracting a flat line to each column of the curvature-corrected data frames, obtained by fitting all pixels further than six pixels from the central row. We also performed a double iteration of outlier rejection in time with a threshold of 10f noise by subtracting the mean from each column, excluding the region 6 pixels away from the trace, sigma-clipping outliers at 3σ. We extracted the 1D stellar spectra using a 4-pixel-wide aperture on either side of the trace centre.The second Eureka! reduction (Eureka! [V2]) used the ‘rateints’ outputs of the JWST Calibration Pipeline and applied Stage 2 of Eureka! as described above, with a modified version of Stage 3. In this reduction, we corrected the curvature of the trace using a spline and found the centre of the trace using the median of each column. We removed 1/51:Limb-darkening is a function of the physical structure of the star that results in variations in the specific intensity of the light from the centre of the star to the limb, such that the limb looks darker than the centre. This is because of the change in depth of the stellar atmosphere being observed. At the limb of the star, the region of the atmosphere being observed at slant geometry is at higher altitudes and lower density, and thus lower temperatures, compared with the deeper atmosphere observed at the centre of the star, at which hotter, denser layers are observed. The effect of limb-darkening is most prominent at shorter wavelengths, resulting in a more U-shaped light curve compared with the flat-bottomed light curves observed at longer wavelengths. To account for the effects of limb-darkening on the time-series light curves, we used analytical approximations for computing the ratio of the mean intensity to the central intensity of the star. The most commonly used limb-darkening laws for exoplanet transit light curves are the quadratic, square-root and nonlinear four-parameter lawsQuadratic:Square-root:Nonlinear four-parameter:I(1) is the specific intensity in the centre of the disk, u1, u2, s1, s2, c1, c2, c3 and c4 are the limb-darkening coefficients and μ = cos(γ), in which γ is the angle between the line of sight and the emergent intensity.in which 52 for Teff = 5,512 K, log(g) = 4.47 cgs and Fe/H = 0.0, along with the instrument throughput to determine I and μ. As instrument commissioning showed that the throughput was higher than the pre-mission expectations53, a custom throughput was produced from the median of the measured ExoTiC-JEDI [V2] stellar spectra, divided by the stellar model and Gaussian smoothed.The limb-darkening coefficients depend on the particular stellar atmosphere and therefore vary from star to star. For consistency across all of the light-curve fitting, we used 3D stellar models55 package, which can compute the linear, quadratic and three-parameter and four-parameter nonlinear limb-darkening coefficients56. To compute and fit for the coefficients from the square-root law, we used previously outlined formalisms58. We highlight that we do not see any dependence in our transmission spectra on the limb-darkening procedure used across our independent reductions and analyses.For the limb-darkening coefficients that were held fixed, we used the values computed using the ExoTiC-LD37. In many of our analyses, we detrended the raw broadband and spectroscopic light curves using the time-dependent decorrelation parameters for the change in the FWHM of the spectral trace or the shift in x-pixel and y-pixel positions . We also tested wider and narrower binning schemes but found that 10-pixel-wide bins achieved the best compromise between the noise in the spectrum and showcasing the abilities of G395H across analyses. In our analyses, we treated the NRS1 and NRS2 light curves independently to account for differing systematics across the two detectors. To construct the full NIRSpec G395H transmission spectrum of WASP-39b, we fitted the NRS1 and NRS2 broadband and spectroscopic light curves using 11 independent light-curve-fitting codes, which are detailed below. When starting values were required, all analyses used the same system parametersT0) of T0 = 2,459,791.612039 ± 0.000017 BJDTDB and T0 = 2,459,791.6120689 ± 0.000021 BJDTDB computed from the NRS1 and NRS2 broadband light curves, respectively; most of the fitting pipelines obtained T0 within 1σ of the quoted uncertainty.Using fitting pipeline 1, we measured a centre of transit time (− on NRS1 and 17.75 e− on NRS2), gain (1.42 for NRS1 and 1.62 for NRS2) and the background counts (which are found to be negligible after cleaning) and compared it to the final signal-to-noise ratio in our light curves  and calculating the signal-to-noise ratio achieved60. Extended Data Fig. 54).For each of our analyses, we computed the expected photon noise from the raw counts taking into account the instrument read noise . We simultaneously fitted a batman transit model62 with a constant baseline and systematics models for data pre-tilt and post-tilt event, fixing the centre of transit time T0, the ratio of the semi-major axis to stellar radius a/R⋆ and the inclination i to the broadband light-curve best-fit values. The systematics models included a linear regression on x and y, for which x and y are the measured trace positions in the dispersion and cross-dispersion directions, respectively. We accounted for the tilt event by normalizing the light curve pre-tilt by the median pre-transit flux and normalizing the light curve post-tilt by the median post-transit flux. We discarded the first 15 integrations and the three integrations during the tilt event. Fourteen-pixel columns were discarded owing to outlier pixels directly on the trace. We fixed the limb-darkening coefficients to the four-parameter nonlinear law.We fitted the broadband and spectroscopic light curves produced from the ExoTIC-JEDI [V3] stellar spectra using the least-squares optimizer, scipy.optimize lm 64, implemented through george65, and a Markov chain Monte Carlo method, implemented through emcee66. For our Tiberius spectroscopic light curves, we held a/R⋆, i and T0 fixed to the values determined from the broadband light-curve fits and applied a systematics correction from the broadband light-curve fit to aid in fitting the mirror-tilt event. We fitted the spectroscopic light curves using a GP with an exponential squared kernel for the same systematics detrending parameters detailed above. We used a Gaussian prior for a/R⋆ and uniform priors for all other fitted parameters.We used the broadband light curves generated from the Tiberius stellar spectra and fitted for the ratio of the planet to stellar radii 67. We fitted each light curve with a two-component function consisting of a transit model (generated using batman) multiplied by a systematics model. Our systematics model included the x-pixel and y-pixel positions on the detector . To capture the amplitude of the tilt event in our systematics model, we carried out piecewise linear regression on the out-of-transit baseline pre-tilt and post-tilt. We first fit the broadband light curve by fixing P and e and fitting for T0, a/R⋆, i, Rp/R⋆, stellar baseline flux and systematic trends using wide uniform priors. For the spectroscopic light curves, we fixed T0, a/R⋆ and i to the best-fit values from the broadband light curve and fit for Rp/R⋆. We held the nonlinear limb-darkening coefficients fixed.We used transit light curves from the ExoTiC-JEDI [V1] stellar spectra and fit the broadband and spectroscopic light curves using the least-squares minimizer LMFIT68 in parallel with the light-curve-fitting module of the transitspectroscopy pipeline48 with dynamic nested sampling through dynesty69 and analytical transit models computed using batman. We fit the broadband light curves for NRS1 and NRS2 individually, fixing P, e and ω and fitting for the impact parameter b, as well as T0, a/R⋆, Rp/R⋆, extra jitter and the mean out-of-transit flux. We also fitted two linear regressors, a simple slope and a ‘jump’ (a regressor with zeros before the tilt event and ones after the tilt event), scaled to fit the data. We fitted the square-root-law limb-darkening coefficients using the Kipping sampling scheme. We fitted the spectroscopic light curves at the native resolution of the instrument, fixing T0, a/R⋆ and b. We used the broadband light-curve systematics model for the spectroscopic light curve, with wide uniform priors for each wavelength bin, and set truncated normal priors for the square-root-law limb-darkening coefficients. We also fitted a jitter term added in quadrature to the error bars at each wavelength with a log-uniform prior between 10 and 1,000 ppm. We computed the mean of the limb-darkening coefficients by first computing the nonlinear coefficients from ATLAS models70 and passing them through the SPAM algorithm71. We binned the data into 10-pixel-wavelength bins after fitting the native-resolution light curves.We fit the broadband and spectroscopic light curves produced from the transitspectroscopy stellar spectra, running juliet75. ExoTEP uses batman to generate analytical light-curve models, adds an analytical instrument systematics model along with a photometric scatter parameter and fits for the best-fit parameters and their uncertainties using emcee. Before fitting, we cleaned the light curves by running ten iterations of 5σ clipping using a running median of window length 20 on the flux, x-pixel and y-pixel shifts and the ‘ydriftwidth’ data product from Eureka! Stage 3 . Our systematics model consisted of a linear trend in time with a ‘jump’ (constant offset) after the tilt event. The ‘ydriftwidth’ was used before the fit to locate the tilt event. We used a running median of ‘ydriftwidth’ to search for the largest offset and flagged every data point after the tilt event so that they would receive a constant ‘jump’ offset in our systematics model. We also removed the first point of the tilt event in our fits, as it was not captured by the ‘jump’ model. We fitted the broadband light curves, fitting for Rp/R⋆, photometric scatter, T0, b, a/R⋆, the quadratic limb-darkening coefficients and the systematics model parameters . We used uninformative flat priors on all the parameters. The orbital parameters were fixed to the best-fit broadband light curve values for the subsequent spectroscopic light-curve fits.We fitted the transit light curves from the Eureka! [V1] stellar spectra using the ExoTEP analysis frameworkx-pixel and y-pixel shifts and an exponential ramp function. The tilt event was accounted for by decorrelating the light curves with the y-pixel shifts, using a (1 + constant × y-shift) term with the constant fitted for in each light curve. For the broadband light-curve fits, we fixed P and fitted for T0, i, Rp/R⋆, a/R⋆, x-pixel and y-pixel shifts and the exponential ramp amplitude and timescale. We fixed the nonlinear limb-darkening coefficients. For the spectroscopic light-curve fits, we fixed the values of T0, i and a/R⋆ and the exponential ramp timescale to the broadband light-curve-fit values, and fitted for Rp/R⋆, the x-pixel and y-pixel shifts and the ramp amplitude. Wide, uniform priors were used on all the fitting parameters in both the broadband and spectroscopic light-curve fits.We fitted transit light curves from the ExoTiC-JEDI [V1] stellar spectra using a custom lmfit light-curve-fitting code. The final systematic detrending model included a batman analytical transit model multiplied by a systematics model consisting of a linear stellar baseline term, a linear term for the 75) to generate the transit model with emcee as the sampler. We calculated the nonlinear four-parameter limb-darkening coefficients using ExoTHETyS (ref. 76), which relies on PHOENIX 2012–2013 stellar models78, and fixed these in our fits to the precomputed theoretical values. Our full transit + systematics model included a transit model multiplied by a second-order polynomial in the time domain. We accounted for the tilt event by subtracting the mean of the last 30 integrations of the pre-transit data from the mean of the first 30 integrations of the post-transit data, to account for the jump in flux, shifting the post-transit light curve upwards by the jump value. We fitted for the systematics , Rp/R⋆ and T0. We used uniform priors for all the fitted parameters. We adopted the root mean square of the out-of-transit data as the error bars for the light-curve data points to account for the scatter in the data.We fitted transit light curves from the Eureka! [V2] stellar spectra, using PyLightcurve . We used a GP systematics model with a time-dependent Matérn (ν = 3/2) kernel and a variable white-noise jitter term. The mean function consists of a linear trend in time plus a sigmoid function to account for the drop in measured flux that occurred mid-transit owing to the mirror-tilt event. For the transit model, we used the exoplanet package80, making use of previously developed light-curve models82. For the GP systematics component, a generalization of the algorithm used by the celerite package83 was adapted for JAX. We fixed both of the quadratic limb-darkening coefficients. For the initial broadband light-curve fit, both NRS1 and NRS2 were fitted simultaneously. All transit parameters were shared across both light curves, except for Rp/R⋆, which was allowed to vary for NRS1 and NRS2 independently. We fitted for T0, the transit duration b and both Rp/R⋆ values. For the spectroscopic light-curve fits, all transit parameters were then fixed to the maximum-likelihood values determined from the broadband fit, except for Rp/R⋆, which was allowed to vary for each wavelength bin. Uncertainties for the transit model parameters, including Rp/R⋆, were assumed to be Gaussian and estimated using the Fisher information matrix at the location of the maximum-likelihood solution, which was computed exactly using the JAX automatic differentiation framework.We fitted the transit light curves from the ExoTiC-JEDI [V2] stellar spectra and performed our model fitting using automatic differentiation implemented with JAX . A linear trend in time was used as a systematics model and a step function was used to account for the tilt event. We fixed On the basis of the independent light-curve fits described above, we produced 11 transmission spectra from our NIRSpec G395H observations using several analyses and fitting methods. Extended Data Table σ of each other, with the uncertainty per bin typically overestimated compared with the mean uncertainty across all reductions.We computed the standard deviation of the 11 spectra in each wavelength bin and compared this to the mean uncertainty obtained in that bin. The average standard deviation in each bin across all fitting pipelines was 199 ppm, compared with an average uncertainty of 221 ppm (which ranged from 131 to 625 ppm across the bins). The computed standard deviation in each bin across all pipelines ranged from 85 to 1,040 ppm, with greater than 98% of the bins having a standard deviation lower than 500 ppm. We see an increase in scatter at longer wavelengths, with the structure of the scatter following closely with the measured stellar flux, for which throughput beyond 3.8 μm combines with decreasing stellar flux. The unweighted mean uncertainty of all 11 transmission spectra follows a similar structure to the standard deviation, with the uncertainty increasing at longer wavelengths. The uncertainties from each fitting pipeline are consistent to within 3σ2, in which σ is the uncertainty on the data point from each reduction). For this weighted-average transmission spectrum, the unweighted mean of the uncertainties in each bin was used to represent the error bar on each point. By using the weighted average of all 11 independently obtained transmission spectra, we therefore do not apply infinite weight to any one reduction in our interpretation of the atmosphere. Although the weighted average could be sensitive to any one spectrum with underestimated uncertainties, we find that our uncertainties are typically overestimated compared with the average. Similarly, we chose to use the mean rather than the median of the transmission spectral uncertainties, as this results in a more conservative estimate of the uncertainties in each bin. We find that all of the 11 transmission spectra are within 2.95σ of the weighted-average transmission spectrum without applying offsets.From all 11 transmission spectra, we computed a weighted-average transmission spectrum using the transit depth values from all reductions in each bin weighted by 1/variance . We performed a Kolmogorov–Smirnov test on each of the normalized residuals and found that they are all approximately symmetric around their means, with normal distributions. This confirms that they are Gaussian in shape, with the null hypothesis that they are not Gaussian strongly rejected in the majority of cases . The second cluster is positively offset from the mean by about 120 ppm and contains most of the transmission spectra produced. We see no obvious trends in this group to any specific reduction or fitting process. The final cluster is centred around the mean but has a broad distribution, suggesting a larger scatter both above and below the average transmission spectrum. This is probably the result of uncleaned outliers or marginal offsets between NRS1 and NRS2. These transmission spectra demonstrate that the 11 independent fitting pipelines are able to accurately reproduce the same transmission spectral feature structures, further highlighting the minimized impact of systematics on the time-series light curves. We suspect that the minor differences resulting from different reduction products and fitting pipelines are linked to the superbias and treatment of 1/84. Each model transmission spectrum from the grids was binned to the same resolution as that of the observations to compute the χ2 per data point, with a wavelength-independent transit depth offset as the free parameter. In general, the forward model grids fit the main features of the data but are unable to place statistically significant constraints on many of the atmospheric parameters, owing to both the finite nature of the forward model grid spacing13 and the insensitivity of some of these parameters to the 3–5-μm transmission spectrum of WASP-39b .To identify spectral absorption features, we compared the resulting weighted-average transmission spectrum of WASP-39b to several 1D RCTE atmosphere models from three independent model grids. Each forward model is computed on a set of common physical parameters , shown in Extended Data Table 88, which consists of model transmission spectra for different day–night energy redistribution factors, atmospheric metallicities, C/O ratios, haze factors and grey cloud factors with a range of line lists and pressure-broadening sources88. In total, there were 5,160 models. Within this grid, we find the best-fit model to have 3 times solar metallicity, with a C/O ratio of 0.35 and a grey cloud opacity 5 times the strength of H2 Rayleigh scattering at 350 nm and a χ2/N = 1.098 for N = 344 data points and only fitting for an absolute altitude change in y.We used the ATMO RCTE grid91, varying the heat redistribution of the planet, atmospheric metallicity, C/O ratio, internal temperature, the presence of aerosols and the atmospheric chemistry (equilibrium or rainout). Opacities used include the BT2 H2O line list92, as well as HITRAN for 129 other main molecular absorbers93 and Kurucz and Bell data for atomic species94. The HITRAN line lists available in this version of PHOENIX are often complete only at room temperature, which may be the cause of the apparent shift in the CO2 spectral feature compared with the other grids that primarily use HITEMP and ExoMol lists. This shift is the cause of the difference in χ2 between PHOENIX and the other model grids. In total, there were 1,116 models. Within this grid, the best-fit model has 10 times solar metallicity, a C/O ratio of 0.3, an internal temperature of 400 K, rainout chemistry and a cloud deck top at 0.3 mbar. The best-fit model has a χ2/N = 1.203 for N = 344 data points.We calculated a grid of transmission spectra using the PHOENIX atmosphere model96), which has its heritage in the Fortran-based EGP mode98, to compute a grid of 1D pressure–temperature models for WASP-39b. The opacity sources included in PICASO 3.0 are listed in Extended Data Table 2O (ref. 99), CO2 (ref. 100), CH4 (ref. 101) and CO (ref. 102). The parameters varied in this grid of models include the interior temperature of the planet (Tint), atmospheric metallicity, C/O ratio and the dayside-to-nightside heat redistribution factor . Virga requires a vertical mixing parameter, Kzz (cm2 s−1), and a vertically constant sedimentation efficiency parameter, fsed. In general, fsed controls the vertical extent of the cloud opacity, with low values (fsed < 1) creating large, vertically extended cloud decks with small particle sizes. In total, there were 3,840 cloudy models. The best fit from our grid with Virga-computed clouds has 3 times solar metallicity, solar C/O (0.458) and fsed = 0.6, which results in a χ2/N = 1.084.We used the open-source radiative–convective equilibrium model PICASO 3.0  or we fit for the cloud-top pressure corresponding to a grey cloud deck with infinite opacity . These results are described in the following section.As well as the grid fit, we also use the PICASO framework to quantify the feature-detection significance. In this method, we are able to incorporate clouds on the fly using the fitting routine PyMultiNest2 and He (via continuum) and CO, H2O, H2S, CO2 and CH4. More minor sources of opacity with VMR abundances <1 ppm are molecules such as OCS and NH3. For example, removing H2S, NH3 and OCS from the single best-fit PICASO 3.0 model increases the chi-square value by less than 0.002. Therefore, we focus on computing the statistical significance of only H2O, SO2, CO2, CH4 and CO.From the chemical equilibrium results of the single best-fit models, the molecules that could potentially contribute to the spectrum based on their abundances and 3–5-μm opacity sources are H31. In this method, we subtracted the best-fit model without a specific opacity source from the weighted-average spectrum of WASP-39b, isolating the supposed spectral feature. We then fit a three-parameter or four-parameter Gaussian curve to the residual data using a nested sampling algorithm to calculate the Bayesian evidence106. For H2O and CO, the extra transit depth offset parameter for the Gaussian fit was necessary to account for local mismatch of the fit to the continuum, whereas only a mean, standard deviation and scale parameter were required for a residual fit to the other molecules. We then compared this to the Bayesian evidence of a flat line to find the Bayes factor between a model that fits the spectral feature versus a model that excludes the spectral feature. These fits are shown in Extended Data Fig. To quantify the statistical significance, we performed two different tests. First, we used a Gaussian residual fitting analysis, as used in other JTEC ERS analyses107. Those values are shown in Extended Data Table Although the Gaussian residual fitting method is useful for quantifying the presence of potentially unknown spectral features, it cannot robustly determine the source of any given opacity. We therefore used the Bayesian fitting routine from PyMultiNest in the PICASO 3.0 framework to refit the grid parameters, while excluding the opacity contribution from the species in question. Then, we compared the significance of the molecule through a Bayes factor analysisσ) for H2O, CO2 and SO2. In general, the two methods only agree well for molecules whose contribution has a Gaussian shape . For example, for CO2, we find decisive 28.5σ and 26.9σ detections for the Bayes factor and Gaussian analysis, respectively. Similarly, for H2O, we find 21.5σ and 16.5σ detections, respectively. The evidence for SO2 is less substantial, but both methods give significant detections of 4.8σ and 3.5σ, respectively. Although the Gaussian fitting method found a broad 1-μm-wide residual in the region of CO , its shape was unlike that seen with the PRISM data31. CO remained undetected with the Bayesian fitting analysis and therefore we are unable to robustly confirm evidence of CO. Similarly, no evidence for CH4 was found23. Gaussian residual fitting in the region of CH4 absorption only found a very broad inverse Gaussian and so is not included in Extended Data Table We find significant evidence >3σ for H2O2 required to match the observations. We add SO2 opacity using the ExoMol line list108, without rerunning the RCTE model to self-consistently compute a new climate profile. Fitting for the cloud deck dynamically, without SO2, produces a single best estimate of 10 times solar metallicity, sub-solar C/O (0.229), resulting in a marginally worse χ2/N = 1.11. With SO2, the single best fit tends back to 3 times solar metallicity, solar C/O. This suggests that cloud treatment and the exclusion of spectrally active molecules have an effect on the resultant physical interpretation of bulk atmospheric parameters. Ultimately, if we fit for SO2 in our PyMultiNest framework with the Virga cloud treatment, we obtain 3 times solar metallicity, solar C/O, log SO2 = −5.6 ± 0.1 (SO2 = 2.5 ± 0.65 ppm) and χ2/N = 1.02, which is our single best-fit model .We performed an injection test with the PICASO best-fit model in the PyMultiNest fitting framework to determine the abundance of SO2 absorption to explain the 4.1-μm spectral feature, we also computed models with prescribed, vertically uniform SO2 VMRs of 0, 1, 5 and 10 ppm using the structure from the best-fit PHOENIX model . We calculated ad hoc spectra using the gCMCRT radiative transfer code109 with the ExoMol SO2 line list108 , C2H2 (ref. 111), C2H4 (ref. 112), C2H6 (ref. 113), CO (ref. 114), CO2 (ref. 100), CS2 (ref. 113), CN (ref. 115), HCN (ref. 116), HCl (ref. 113), H2S (ref. 117), HF (ref. 118), H3+ (ref. 119), LiCl (ref. 115), NH3 (ref. 120), NO (ref. 114), NO2 (ref. 113), N2O (ref. 114), N2 (ref. 121), NaCl (ref. 122), OCS (ref. 113), PH3 (ref. 123), PN (ref. 124), PO (ref. 125), SH (ref. 126), SiS (ref. 127), SiH4 (ref. 128), SiO (ref. 129), the X–X state of SO (ref. 130), SO2 (ref. 108), SO3 (ref. 108) and isotopologues of H2O, CH4, CO2 and CO, but did not find a convincing candidate that showed opacity at the correct wavelength or the correct width. The narrowness of the feature suggests that it could be a very distinct Q-branch, in which the rotational quantum number in the ground state is the same as the rotational quantum number in the excited state. However, of the molecules we explored, there were no candidates with a distinct Q-branch at this wavelength whose P-branch and R-branch did not obstruct the neighbouring CO2 and continuum-like CO + H2O opacity.A 0.08-μm-wide bump in transit depth centred at 4.56 μm is not fit by any of the model grids. This feature, picked up by the resolution of G395H, is not clearly seen in other ERS observations of WASP-39b. Following the same Gaussian residual fitting procedure as described above, we found a feature significance of 3.32 are available in HITRAN2020 (ref. 113) but not in ExoMol131. Furthermore, if photochemistry is important for WASP-39b, as indicated by the presence of SO2, then there may be many species out of equilibrium that may contribute to the transit spectrum, some of which do not have high-temperature opacity data at present . Future observations over this wavelength region of this and other planets may confirm or refute the presence of this unknown absorber.We also note that many of these species lack high-temperature line-list data, making it difficult to definitively rule out such species. For example, OCS, SO and CSAny methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-05591-3.Peer Review File