Intracortical neural probes are both a powerful tool in basic neuroscience studies of brain function and a critical component of brain computer interfaces (BCIs) designed to restore function to paralyzed patients. Intracortical neural probes can be used both to detect neural activity at single unit resolution and to stimulate small populations of neurons with high resolution. Unfortunately, intracortical neural probes tend to fail at chronic timepoints in large part due to the neuroinflammatory response that follows implantation and persistent dwelling in the cortex. Many promising approaches are under development to circumvent the inflammatory response, including the development of less inflammatory materials/device designs and the delivery of antioxidant or anti-inflammatory therapies. Here, we report on our recent efforts to integrate the neuroprotective effects of both a dynamically softening polymer substrate designed to minimize tissue strain and localized drug delivery at the intracortical neural probe/tissue interface through the incorporation of microfluidic channels within the probe. The fabrication process and device design were both optimized with respect to the resulting device mechanical properties, stability, and microfluidic functionality. The optimized devices were successfully able to deliver an antioxidant solution throughout a six-week in vivo rat study. Histological data indicated that a multi-outlet design was most effective at reducing markers of inflammation. The ability to reduce inflammation through a combined approach of drug delivery and soft materials as a platform technology allows future studies to explore additional therapeutics to further enhance intracortical neural probes performance and longevity for clinical applications. Properly functioning intracortical neural probes are crucial for their inclusion in approaches to improve patient quality of life for amputees and individuals with tetraplegia resulting from injury or disease ,4,5,6,7.The act of implantation and the chronic residence of recording electrodes in brain tissue activates a cell-driven neuroinflammatory response, prompting (among other things) an increased production of reactive oxygen species (ROS). ROS contribute to both biological and material failure mechanisms by damaging not only cells in the direct vicinity of the implant, but the implant substrate itself ,12,13. BPrevious studies in our laboratories have demonstrated that both highly compliant neural probe structures and local anti-oxidant delivery attenuate the inflammatory responses to the implant ,30,31,32Separately, systemic or ventricular delivery of the natural anti-oxidant resveratrol yielded improved neural density and decreased oxidative stress, indicating decreased neural inflammation ,36. ResvDirect delivery to the brain matter at the site of microelectrode implantation bypasses the BBB and allows efficient and consistent delivery of the required doses for therapeutic effect. Rigid silicon microelectrode devices with fused capillary catheters or integrated microfluidic channels have demonstrated combined intracortical recording and local drug release during intraoperative studies ,38,39,40Here, we report on the development of a NC-based neural probe for sustained resveratrol delivery. We explored three methods of device fabrication consisting of two traditional methods used in the literature for microdevice fabrication, and one hybrid method ,49. The The process of fabricating NC-based microfluidic neural probe channel layers is shown in An SU-8 2015  channel pattern was photolithographically defined on a 100 mm-diameter silicon wafer to serve as a positive mold for a polydimethylsiloxane (PDMS) negative mold. Sylgard 184 elastomer was mixed in a 10:1 ratio and degassed in a desiccator chamber connected to a vacuum for 30 min while the SU-8 mold was placed in a PTFE dish. After degassing, 330 g of PDMS prepolymer solution was carefully poured over the mold and cured in the oven at 60 °C for 1 h, resulting in a 3 mm-thick mold. The oven was then turned off and the mold was allowed to cool slowly overnight. Once the PDMS mold was completely cool, it was removed from both the PTFE dish and the SU-8 mold, for use in subsequent steps.w/w film of tCNCs within the PVAc [The same nanocomposite (NC) composition was used for all films and followed established protocols  26,50,5,550,51. After DMF evaporation, mold-only films were carefully separated from the PDMS mold using forceps C. To preThree methods were used to form microfluidic channels within NC. Emboss-only films were cast on planar PDMS slabs, then separated from the PDMS slab with the aid of forceps and water-induced softening B. The fiCover layers were created similar to emboss-only films, where the NC solution was cast into a Teflon dish with a flat PDMS slab. For mold-only films, the cover layers were then taken off the PDMS slabs carefully with forceps. All cover layer films, regardless of use for mold-only or mold-emboss channel layer film were first pressed before removing from the PDMS slabs.The resulting films of all methods were 100 mm in diameter and consisted of 84 rectangular chips (schematic representation shown in n = 6 per group) and width (n = 15 per group) dimensions  to determine channel depth  of ASTM D1708 standard test specimens was used to assess the effects of compression on the mechanical properties of the films. Samples were laser-micromachined from as-cast or pressed planar films to correspond to the fabrication conditions used for the mold-only and mold-embossed channel layer fabrication methods, respectively. The lateral dimensions of each sample were confirmed by measuring them under a microscope before testing. The thickness of each sample was measured using a micrometer. Samples were tested using a clamp force of 70 psi and a tensile strain rate of 10%/min. Each group had at least three replicates.Cover layers with inlet holes were laser-micromachined from planar NC films. Channel and cover layer film chips approximately 7 mm × 9 mm in size were soaked overnight in DI water at 20 °C to plasticize the films. The water-saturated films were aligned and thermally bonded on a hotplate at 50 °C for 15 min A. Each bThree NC microfluidic neural probe designs were evaluated in vivo . Each miCustom 3D-printed microfluidic connectors facilitated interfacing the inlets of individual probes to PE-50 tubing , and Alzet osmotic pumps  to make a completed device . The 3D-The ability of packaged probes to transport fluids through the microfluidic channel was assessed by pushing air through the probe with a syringe controlled by a syringe pump, measuring the force on the syringe, and observing the appearance and location of air bubbles in the probes . Packagebest-performing design to implement in future studies. The evaluation of “best-performing” will consider both the robustness of the device design and the in vivo histological response.Single-outlet probes with 100 µm-wide channels were connected and implanted into Sprague–Dawley rats for up to 4 weeks (N = 5), or with single-outlet (N = 3) or double-outlet (N = 4) 50 µm-wide channels for 6 weeks. Animals were implanted with packaged probes made using the mold-emboss method and connected to osmotic pumps with the flow of 30 µM solution of resveratrol in saline with a rate of 0.15 µL/h. The designs were compared to a control: a NC microfluidic probe cut to the same dimensions of the experimental probes (S50-me), but not connected to an osmotic pump (N = 4). The osmotic pump with the 0.15 µL/h flow rate was chosen by taking into account the therapeutic dose necessary of 30 µM, and the interstitial fluid (ISF) clearance rate ,56,57,58Packaged probes  were sterilized using cold ethylene oxide gas. In a sterile environment, Alzet osmotic pump reservoirs  were weighed before and after filling with nominally 200 µL of a sterile solution of 30 µM trans-resveratrol  in saline, to determine the initial volume of the solution. The pumps were then connected to the tubing of the packaged probes and placed in a 50 mL conical tube filled partly with sterile saline solution such that only the pump was submerged in the fluid. The conical tube was sealed and allowed to continue priming in an incubator at 37 °C for 60 h to ensure the solution was within the microfluidic channel prior to implantation.Surgical implantation in Sprague–Dawley rats followed our established protocols ,60,61,62The anesthetic plane was tested once again before the initial midline incision on the scalp, and the periosteum was cleaned off using cotton-tipped applicators to expose the skull. Hydrogen peroxide was applied to dehydrate the skull, and Vetbond  was applied to prime the skull for the craniotomy to ensure smooth drilling. Using a 1.45 mm drill bit on a Kopf dental drill , craniotomies were made at 3 mm lateral and 3 mm anterior to the bregma. The drilling duration was pulsed to avoid the generation to thermal damage to the blood–brain barrier ,64. A suPrimed microfluidic probes were secured using a custom holder attached to the stereotaxic frame. The probes were then lowered 3 mm into the motor cortex and the craniotomy was sealed with a layer of Kwik-Cast  and Teets Cold Cure Dental Cement . The osmotic pump was placed into the subcutaneous pocket, taking care to lay the tubing flat. A headcap was made using dental cement to encompass the probe connector and part of the tubing to ensure secure attachment. The incision site was sutured closed, and the animals were monitored 5 days post-operatively to ensure proper healing. At the end of the study timepoint, the animals were sacrificed via transcardial perfusion. Intraperitoneal (IP) injections of ketamine (80 mg/kg) and xylazine (10 mg/kg) were administered to the animals and allowed to reach an aesthetic plane, which was confirmed with a toe pinch. A peristaltic pump  was used to pump 500–800 mL 1X PBS first, and then 300–500 mL 10% phosphate buffered formalin  at a rate of 8.3 mL/min. The brain was harvested, along with the skull attached to the headcap. Saline was used to gently wash explanted probes recovered post-mortem. The explanted osmotic pump reservoirs were also collected and stored.Explanted probes were washed with 1X PBS + 0.1% triton briefly, and then incubated in DAPI to stain any cellular nuclei adhered to the probes. The probes were then dried and imaged using a confocal microscope  to determine if there were any adhered cells on the surface of the probes or inside the channels. After fluorescent confocal microscopy, the probes were then placed under an optical microscope  and imaged at high magnification.3) [Resveratrol solution remaining in the osmotic pumps at the given timepoint was removed from the explanted pump using 27 g needles and placed into 1 mL Eppendorf tubes, according to instructions from Alzet . The ini3) . The tubHistology protocols for neuronal nuclei density , blood–brain barrier permeability , immune cell activation , and glial cell activation  were similar to those published in previous studies in our laboratory ,36,66,67An automated immunohistochemistry stainer  was used to stain for oxidized proteins . The slides were removed from −80 °C deep freezer and allowed to acclimate to room temperature in a humidity chamber for 30 min and washed with three consecutive 1X PBS washes prior to placing in the Bond Rx to remove OTC. The slides were then washed with the Bond Rx proprietary wash buffer and underwent epitope retrieval also using the company’s proprietary epitope retrieval solution. Peroxide buffer was applied and washed off, and primary antibodies were applied. After the primary were washed, the slides were taken out of the Bond Rx to be incubated in secondary antibody solution for two hours. Copper sulfate solution was applied for 10 min before washing with DI water and mounting with Fluoromount-G. The slides were dried in a cool, dark area before imaging.Axioscan Z1  with the 20X objective was used to fluorescently image the slides at the same exposure times for each fluorescent marker. The resulting images were subset to reduce file size and exported as a TIFF file for further analysis using a custom MATLAB algorithm called SECOND. In this software, the implant holes were marked and any artifacts were excluded before intensity analysis was conducted . Anotherp < 0.001) but were closer to the depth measurements of the mold. The mold-only films had the deepest channels after channel layer film fabrication. High-magnification optical microscopy imaging showed that the widths of the films resulting from each fabrication method ranged from 2.8% to 10% larger than the PDMS mold  . Based ocollapse ,72,73. Wp = 0.054, the differences are not statistically significant, but it is worth noting that the double outlet probes seemed to have higher elution volumes than single outlet probe designs. On ex vivo inspection for signs of occlusion, some debris were apparent in DAPI-stained S50-me probes, but no debris were seen for the double-outlet D50-me probes  permeability, and neuronal nuclei density. Other resveratrol delivery studies performed in the laboratory have shown improvements in neuronal density, but not for glial scarring ,16,29. AExplanted probes that were stained with DAPI observed that the side placement of the outlets decreased cellular debris inside of the channels , whereasThe primary objective of this work was to optimize fabrication methods and design for microfluidic channels integrated into a mechanically adaptive polymer nanocomposite. The optimized fabrication method combined micromolding and embossing to form the channel layer, which was then bonded to a cover layer. Though the mold-only process initially produced channels that better matched the inverse of the PDMS mold, the process of soaking and thermally bonding to the cover layers resulted in considerable deformation and collapsing of the channels. Additionally, mold-only devices required more than twice the force to push air through the channel and had a lower “success” yield compared to the mold-emboss process. Material testing confirmed that the process of embossing yielded a higher modulus compared to mold-only films, which contributed to the improved stability of the mold-embossed devices in terms of the ability to maintain the channel geometry during the thermal bonding step. The results of the characterization determined that the best method for fabricating the probes was the mold-embossed method.The in vivo study showed that these probes, when connected to osmotic pumps, were able to deliver resveratrol solution continuously and consistently to the site of implantation over the course of 6 weeks. Additionally, the double-outlet branching design showed the best performance in histology neuroinflammation metrics compared to the single outlet design, supporting such designs for future full studies. Our design allows multiple avenues of drug delivery to the site of implantation by having multiple outlets and a permeable mechanically adaptive material that can diffuse drug through the substrate. Other designs in the field have either used stiff substrates with open ports on the tip, or flexible substrates with open ports ,47. UsinThe mechanically adaptive microfluidic neural probe optimized and characterized in this paper establishes a platform wherein both the drug delivered and the time course of delivery can be altered to fit a patient’s needs. The mechanically softening aspect of the probe substrate aims to reduce neural inflammation, with the goal of producing IMEs with improved performance at chronic time points. The additional delivery of a drug such as an antioxidant may further improve IME performance and longevity. However, the drug may also be swapped out for any therapeutic designed to improve brain tissue health and function. The flexibility of our design lends itself ‘flexible’ to the discovery of additional targets with the recent use of more sophisticated analysis methods of the neuroinflammatory response to IMEs ,80,81,82Performance enhancement given these conditions, in comparison to the industry standard of rigid silicon implants. Previous studies have demonstrated the ability to integrate recording electrodes that can record up to 16 weeks, with dimensions better matching rigid silicon NeuroNexus probes . Therefo


Fluid overload in the critically ill has been a clinical concern and a topical research question in the past decades. Numerous investigations formed in the wake of two landmark trials where higher intravenous fluid volumes led to worse outcome , 2. The The Research Topic comprises six papers covering early fluid resuscitation and vasopressors, fluid management in sub-populations of critically ill patients, fluid overload assessment, and de-resuscitative strategies from clinicians' perspectives.Macdonald et al. provide an overview of contemporary evidence for fluids vs. vasopressors in the early resuscitation of sepsis and septic shock. The review outlines theories for sepsis pathogenesis, including micro- and macro-circulatory alterations, and the potential hemodynamic interactions of intravenous fluids and vasopressors. Ultimately, the rationale for a trial of restricted fluids and early vasopressors in septic shock is outlined.In a narrative review, Zeuthen et al. report the results of an international survey among 1,066 Intensive Care Unit (ICU) physicians on the assessment and treatment of fluid overload. It describes current de-resuscitative strategies in the ICU, including that most clinicians supported a weight-based definition of fluid overload (minimum 5-10% increase in body weight), and most considered fluid overload a modifiable risk for morbidity. The most frequent choice of treatment was diuretics, followed by fluid restriction.Fluid overload has no uniform definition, diagnostic test, or management guideline in the general critically ill population. via afterload reduction may have unloading benefits, but low preload and low blood pressures may also reduce coronary flow. Thus, hemodynamic changes can be both beneficial and detrimental in this patient population. As patients with heart failure often require fluids to improve cardiac output in the initial phase, they may represent a population who is specifically prone to to fluid overload at a later stage. In a retrospective cohort study, Dong et al. evaluated the association between fluid management and in-hospital mortality in ICU patients with sepsis and heart failure. The authors propose fluid accumulation index  as a tool to assess fluid overload, as the fluid accumulation index was found to be a risk factor for in-hospital mortality in these patients.Patients with heart failure present a particular challenge for fluid management. Lower blood pressures Waskowski et al. included 2,158 patients admitted to ICU due to severe heart failure and/or cardiogenic shock in a retrospective cohort study. In contrast to other studies, no association between fluid overload (>5% increase in body weight) at ICU discharge and 30-day mortality was found in this population. Notably, <10% of the study population had fluid overload at ICU discharge, indicating that most of the population was already de-resuscitated at that time. In this study, open-heart surgery prior to admission, prior liver disease, and lactate at baseline were associated with fluid overload at ICU discharge, while disease severity or pre-existing kidney disease were not.Jensen et al. investigated the effect of prehospital transportation on fluid administration in the first 24 h in patients with suspected infection. In this post-hoc analysis of a prospective observational study, the authors found that patients transported with emergency medical services received more fluid than those arriving at the hospital on their own, after adjustment for confounders. Importantly, the majority of the study population had simple infection or sepsis, and these patients are not directly represented in the Surviving Sepsis Guideline (While the bulk of fluid research has focused on early intrahospital management, uideline . Pre-hosWrzosek et al. found that infused fluids and fluid balance on day 2 of ICU stay were associated with increased mortality. Fluid balances and fluid input were correlated with the number of blood products infused, as well as with coagulation parameters, suggesting that hemorrhagic shock might have been a driver of excess fluid administration and poor outcome in this study.Trauma patients have a unique pathophysiology, often characterized by hypovolemic shock followed by systemic inflammation, thus representing a separate sub-population of critically ill patients. In a retrospective study of 52 trauma patients, In summary, this Research Topic covers some of the key aspects of fluid overload in the critically ill and addresses specific populations and time points, where fluid management could impact the patient's trajectory . We emphTM, SW, and AM co-wrote the manuscript. All authors contributed to the article and approved the submitted version.


CAB+RPV LA is the first complete long-acting regimen for virologically suppressed people with HIV (PWH) and demonstrated non-inferiority to standard of care antiretroviral regimens in the Phase 3/3b trials FLAIR, ATLAS, ATLAS-2M, and SOLAR. Implementation of a provider administered regimen poses new delivery challenges and real-world evidence is essential to understand utilization and clinical outcomes. The BEYOND study describes the demographics and month 6 (M6) clinical outcomes of patients initiating CAB+RPV LA in the US.BEYOND is a 2-year observational real-world study of utilization, outcomes, and experience of people with HIV (PWH) initiating CAB+RPV LA (monthly or every 2 months) across 30 US sites. Healthcare providers (HCPs) completed an electronic case report form (eCRF) at baseline and M6 to capture demographics, medical and treatment history, and clinical outcomes.A total of 308 PWH (Table 1) were enrolled between Sep 2021- Jul 2022 and initiated on CAB+RPV LA. As of the data cut-off for this analysis (Jan 2023), 248 PWH had reached M6 of which 25 were reported as having discontinued CAB+RPV LA. The most common HCP reported primary reason for initiating CAB+RPV LA was patient request (41%). At M6, of the 803 injections given after the first injections, 667 (83%) occurred within +/-7 days of the target treatment date and 136 (17%) were outside the target treatment window (median 4 days outside). Of 1087 total injections expected, 44 (4%) were missed; of these, 3 (7%) used oral CAB+RPV and 19 (44%) used other oral regimens to cover missed injections. Of 189 PWH with viral load data available at both baseline and M6 (Table 2), 181 (96%) had viral loads of < 50 copies/mL. Confirmed virologic failure (CVF) occurred in 4 (1.6%), including 1 who had missed injections. Resistance was reported in 2 PWH. Discontinuations due to drug intolerance/injection site reactions were reported in 6 PWH.The M6 results from real world initiation of CAB+RPV LA in the US are consistent with the Phase 3/3b clinical trials with high rates of virologic suppression, low rates of CVFs and treatment emergent resistance, and low rates of discontinuation due to drug intolerance.Gary I. Sinclair, MD, Abbvie: Grant/Research Support|Gilead: Advisor/Consultant|Gilead: Grant/Research Support|Janssen: Advisor/Consultant|Janssen: Grant/Research Support|Janssen: Honoraria|Merck: Advisor/Consultant|Merck: Grant/Research Support|Merck: Honoraria|Thera: Advisor/Consultant|Thera: Grant/Research Support|Thera: Honoraria|ViiV: Advisor/Consultant|ViiV: Grant/Research Support|ViiV: Honoraria Michael Sension, MD, Gilead: Advisor/Consultant|Gilead: Honoraria|Viiv: Advisor/Consultant|Viiv: Grant/Research Support|Viiv: Honoraria Alexandra Dretler, MD, Gilead: Stocks/Bonds|Johnson and Johnson: Stocks/Bonds|Pfizer: Stocks/Bonds Stefan Schneider, MD, ViiV Healthcare: Grant/Research Support Catherine K. Schubert, PharmD, GSK: Stocks/Bonds|ViiV Healthcare: Employee of ViiV Healthcare Deanna Merrill, PharmD, MBA, AAHIVP, ViiV Healthcare: Employment|ViiV Healthcare: Stocks/Bonds Cindy Garris, MS, GSK: Stocks/Bonds|ViiV Healthcare: Employee


Funding statement. The project name A2M2TECH was missing. An additional funding source, The Scientific and Technological Research Council of Türkiye, should also have been included. The correct Funding statement appears below:In the published article, there were errors in the “This work was supported by the Frontier Leading Technology Basic Research Project of Jiangsu (No. BK20202007), the National Natural Science Foundation of China (Grant No.52005262), BK20190463 and No. B18028. This work was also supported by the National Natural Science Foundation of China (grant number 51775281). This project has also received financial support from the European Union’s Horizon 2020 (H2020) research and innovation program under the Marie Skłodowska-Curie, grant agreement No. 764935. This work has also received funding from the H2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 101034425 for the project titled A2M2TECH. This study has also received financial support from The Scientific and Technological Research Council of Türkiye (TUBITAK) with grant No 120C158 for the same A2M2TECH project under the TUBITAK’s 2236/B program.”The authors apologize for these error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


It is known that S‐pindolol attenuates muscle loss in animal models of cancer cachexia and sarcopenia. In cancer cachexia, it also significantly reduced mortality and improved cardiac function, which is strongly compromised in cachectic animals.Here, we tested 3 mg/kg/day of S‐pindolol in two murine cancer cachexia models: pancreatic cancer cachexia (KPC) and Lewis lung carcinoma (LLC).P < 0.05) and around a third of the lean mass lost by tumour‐bearing controls , whereas loss of fat mass was similar. In the LLC model, the gastrocnemius weight was higher in sham (108 ± 16 mg) and S‐pindolol tumour‐bearing (94 ± 15 mg) mice than that in placebo (83 ± 12 mg), whereas the soleus weight was only significantly higher in the S‐pindolol‐treated group (7.9 ± 1.7 mg) than that in placebo (6.5 ± 0.9). Grip strength was significantly improved by S‐pindolol treatment . A higher grip strength was observed in all groups; whereas S‐pindolol‐treated mice improved by 32.7 ± 18.5 g, tumour‐bearing mice only show minimal improvements .Treatment of mice with 3 mg/kg/day of S‐pindolol in KPC or LLC cancer cachexia models significantly attenuated the loss of body weight, including lean mass and muscle weights, leading to improved grip strength compared with placebo‐treated mice. In the KPC model, treated mice lost less than half of the total weight lost by placebo (−0.9 ± 1.0 vs. −2.2 ± 1.4 g for S‐pindolol and placebo, respectively, S‐pindolol is an important candidate for clinical development in the treatment of cancer cachexia that strongly attenuates loss of body weight and lean body mass. This was also seen in the weight of individual muscles and resulted in higher grip strength. It blocks the beta‐1 adenoreceptor, is a partial agonist on the anabolic beta‐2 adenoreceptor and has central effects on the 5‐HTThe aim of the study was the confirmation that 3 mg/kg/day of S‐pindolol also shows efficacy in a second species (mouse) and additional cancer cachexia models (pancreatic cancer cachexia [KPC] and Lewis lung carcinoma [LLC]).n = 8) or orthotropic tumour inoculation (n = 23) of 3 million KPC cells in a volume of 40 μL into the tail of the pancreas, as described before.n = 11) or 3 mg/kg/day of S‐pindolol (n = 12) once daily for 13 days. Body composition, that is, lean and fat mass, was assessed by nuclear magnetic resonance (NMR) spectroscopy  1 day before tumour cell inoculation and on the day of euthanasia.Male C57BL/6J mice were 7–12 weeks of age and 23.9 ± 0.6 g in weight for KPC and 17.5 ± 0.4 g in weight for LLC at the start of experimental procedures. Thirty‐one mice were randomized to sham (n = 5), placebo  or 3 mg/kg/day of S‐pindolol (n = 10) for 13 days. Mice received an intramuscular (hind leg) inoculum of 5 × 105 LLC cells obtained from exponential tumours, as described before.For the LLC model, 30 C57BL/6 mice (mean body weight of 17.5 ± 0.4 g) were randomized to treatment: no tumour injection, sham  and Barcelona .P‐value of <0.05 was considered significant.Results, represented as mean ± SD, were analysed using GraphPad Prism 8.0 . All data were tested for normal distribution using the Kolmogorov–Smirnov test. Group comparisons were performed by one‐way analysis of variance (ANOVA) with adjustment for multiple testing of sham‐treated controls and S‐pindolol‐treated tumour‐bearing animals against tumour‐bearing vehicle controls. A P < 0.05) and around a third of the lean mass lost by tumour‐bearing controls . Fat loss in S‐pindolol‐treated mice was comparable with that in tumour‐bearing controls  and S‐pindolol‐treated animals (1.3 ± 0.8 g). In contrast to sham, tumour‐bearing vehicle controls lost around 10% of their initial body weight over the study and showed falls in mean lean and fat mass of 1.5 and 1 g, respectively. Mice treated with 3 mg/kg/day of S‐pindolol lost less than half of the total weight lost by tumour‐bearing controls  and similar to that of sham‐treated controls , whereas their spleen weight was 53% higher (P < 0.05). S‐pindolol‐treated animals and sham controls showed near identical differences to tumour‐bearing placebo mice for these organs. In contrast, whereas the weights of gastrocnemius and soleus muscles in tumour‐bearing controls were notably lower than in sham controls , these muscles in S‐pindolol‐treated animals were of near identical weight to those of sham‐treated controls and so higher than in tumour‐bearing controls . These results show good conservation of skeletal muscle mass by S‐pindolol (Table The mean heart weight of tumour‐bearing controls was around 10% lower than that of sham controls (ol Table 1.P < 0.05) and showed increased grip strength  and S‐pindolol tumour‐bearing  mice than that in placebo (83 ± 12 mg), whereas the soleus weight was only significantly higher in the S‐pindolol‐treated group  than that in placebo  and S‐pindolol‐treated animals (4.6 ± 0.8 g). Tumour‐bearing vehicle controls lost around 3% of their initial body weight (−0.5 ± 1.1 g), whereas mice treated with 3 mg/kg/day of S‐pindolol gained 3% (0.6 ± 0.6 g, th FigureTable2Treatment of mice with 3 mg/kg/day of S‐pindolol in KPC or LLC cancer cachexia models significantly attenuated the loss of body weight and, in particular, protected lean mass and muscle weights, leading to an improved grip strength compared with placebo‐treated mice. Better muscle function, that is, hand grip strength, is an indicator not only for improved quality of life but also for survival.This study shows that S‐pindolol's positive effects are not limited to a rat model of liver cancer‐associated cachexia.In conclusion, S‐pindolol has shown encouraging pre‐clinical and early clinical data, not only in the Yoshida, KPC and LLC in rodent models but also in an early human phase IIa study that included patients with colorectal and non‐small‐cell lung cancer,JS is a shareholder of Actimed Therapeutics and a paid consultant for Actimed Therapeutics, Pephexia Therapeutics and Drexel Pharma Technologies and received grant support from Boehringer Ingelheim. JMA declares having received honoraria and/or lecture fees from Amgen, Novartis, Smartfish, Rottapharm/Madaus, Numico, Danone, Grunenthal, Fresenius, Merck KGaA, Merk Sharp & Dohme, Serono, Procter and Gamble, Santhera Pharmaceuticals, Bristol Myers Squibb and Nutricia. DLM is a shareholder and Chief Medical Officer for Endevica Bio, Inc., and a consultant for Pfizer, Inc. All other authors have no conflict of interests.


Adropin is a secretory peptide that regulates glucose, lipid, and protein metabolism, which is closely related to obesity, insulin resistance, dyslipidemia, and atherogenesis. The serum adropin level is related to sex and depends upon nutritional preferences. This study aims to determine the association between serum adropin levels and body composition parameters in kidney transplant recipients (KTRs), especially emphasizing sex differences. Our case–control study involved 59 KTRs  who were divided into two groups according to sex, and each group of those KTRs was further divided into higher or lower adropin values than the mean value in each sex group. Univariate regression showed a negative association of adropin levels with most anthropometric and body composition parameters in men’s KTRs. Contrary to this, the serum adropin level was negatively associated only with phase angle in postmenopausal female KTRs. Multivariate regression showed that skeletal muscle mass and phase angle were the only negative predictors in women’s KTRs, whereas in men, negative predictors were BMI and body water. These findings imply that adropin could have a different impact on metabolic homeostasis in KTRs regarding sex and could be considered a negative predictor of body composition in KTRs. Adropin is a recently identified regulatory protein, encoded by energy balance-related genes (Enho) and involved in the maintenance of energy homeostasis ,2. This Recent studies have shown that adropin is involved in the regulation of glucose, lipid, and protein metabolism, which is closely related to obesity, insulin resistance, dyslipidemia, and atherogenesis ,3,6. AniHowever, studies investigating plasma adropin concentrations in humans have indicated some differences between sexes. For example, women have lower serum adropin levels compared to men . AlthougMoreover, the relationship between plasma adropin concentrations and the levels of low-density lipoprotein cholesterol (LDL-C) is also sex-dependent and is only found in men . This efSeveral studies have investigated adropin, focusing on the female population in the generative period. A study based on polycystic ovary syndrome (PCOS), which is associated with obesity, dyslipidemia, and insulin resistance, showed lower serum and follicular fluid (FF) adropin levels in PCOS women compared to control patients of similar age and BMI . ContrarA recent study investigating the role of adropin in autoimmune disease—primary Sjogren syndrome (pSS), which is more common in women—showed that these patients have significantly higher serum adropin levels compared to healthy controls . Also, aAccordingly, there is no study investigating the role of adropin and parameters of body composition in immunocompromised populations as in KTRs. Therefore, our study aimed to assess serum adropin levels and body mass composition parameters in KTRs and to assess sex differences between men and postmenopausal women who are not influenced by hormonal disturbances.This cross-sectional study was conducted at the Outpatient Clinic for Clinical Nutrition, Nephrology and Dialysis Division, Internal Medicine Clinic, University Hospital of Split, Croatia, between July 2020 and October 2020.The study comprised 59 KTRs, aged over 18, with functional graft, no mobility issues, and follow-up for more than a year following kidney transplantation (28 women and 31 men). The exclusion criteria were active inflammatory or malignant disease, history of stroke and myocardial infarction, implanted pacemaker or cardioverter defibrillator, stents, or limb amputation.Baseline clinical data included the patient’s age and sex, smoking status, presence of chronic kidney disease, cardiovascular risk factors , presence of cardiovascular and cerebrovascular disease, duration, and type of dialysis treatment before kidney transplantation. In terms of laboratory parameters, all study participants received standard peripheral blood sampling on the same day as the body composition and blood pressure measurements. Peripheral venous blood samples were collected following overnight fasting and handled according to standard laboratory practice by an experienced medical biochemist blinded to group assignments. The conventional hematological and biochemical parameters were analyzed on the same day. The samples for adropin analysis were centrifuged and maintained at −80 °C until analysis.12), hemoglobin (g/L), mean cellular volume , sodium (mmol/L), potassium (mmol/L), and estimated glomerular filtration rate  using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI). A complete blood count was obtained using a hematology analyzer .We measured the following parameters: urea (mmol/L), creatinine (mmol/L), uric acid (mmol/L), serum albumin (g/L), phosphates (mmol/L), C-reactive protein , calcium (mmol/L), glucose (mmol/L), triglycerides , total cholesterol , LDL-C, (mmol/L), erythrocytes  kit  according to the manufacturer’s instructions. The test range was 0.3–20 ng/mL and sensitivity of 0.08 ng/m, and inter-assay and intra-assay coefficients of variation (CV) within the probe were less than 10%.Each participant’s body composition was analyzed using bioelectrical impedance measurement (BIA) with a Tanita MC-780 Multi Frequency Segmental Body Analyzer . BIA included analysis of these data: body mass (kg), muscle mass (kg and %), skeletal muscle mass (kg and %), fat mass (kg and %), fat-free mass (kg and %), visceral fat, trunk fat mass (kg and %), skeletal muscle mass (kg and %), sarcopenic muscle index (SMI), phase angle (PhA (◦)), total body water , extracellular water  and intracellular water . All patients were requested to refrain from eating or drinking anything for at least three hours before the measurement, to urinate right before the analysis, and to abstain from alcohol, excessive eating or drinking, and excessive training for at least one day before the body composition assessment . AnthropUsing an Agedio B900  oscillometry-based equipment, peripheral and central blood pressure and arterial stiffness were measured. The correct-sized cuff was chosen and precisely placed based on the upper arm circumference. All participants were analyzed while calmly seated, with their backs and arms supported, feet flat on the floor, their legs uncrossed, and their bladders empty. We collected information on peripheral systolic blood pressure , peripheral diastolic blood pressure , peripheral mean arterial pressure , peripheral pulse pressure (pPP), central systolic blood pressure , central diastolic blood pressure , central mean arterial pressure , central pulse pressure (cPP), mmHg), and pulse wave velocity .p-values less than 0.1 were entered into the LASSO regression model, which was used to select the most relevant variables. Finally, a multivariate linear regression model was applied with selected variables after LASSO regression to identify the strongest predictors for serum adropin levels and to obtain beta coefficients, standard errors, and p-values.The normality of the data was first evaluated using the Shapiro–Wilks test. In cases where the data were normally distributed, it was presented with mean and standard deviation (SD), whereas if the data were not normally distributed, it was presented with median and interquartile range (IQR). Categorical data were presented as numbers with percentages. To test the differences between the groups, the chi-square test, T-test, or Mann–Whitney test were applied as appropriate. To evaluate the predictors of serum adropin levels, first, a univariate linear regression was performed, after which all parameters with The study included 59 KTRs of which 31 (53%) were men and 28 (47%) were women. All studied KTR women were in the postmenopausal period. To determine whether there are sex differences depending on higher and lower adropin values, we divided both sex groups into two subgroups depending on the mean adropin value . Therefore, we obtained two subgroups in female KTRs regarding the adropin value: those with lower adropin values (n= 11 (39%)) and women with higher adropin values (n = 17 (61%)). We used the same methodology to categorize men into two subgroups: those with lower adropin values (N = 14 (45%)) and male patients with higher adropin values than the mean value (n = 17 (55%)).p = 0.015; men p = 0.012), BMI , and waist circumference . Therefore, we observed significant differences between the sexes in the relation of adropin levels to body composition. We found only in male KTRs that serum adropin level was significantly related to fat tissue mass (p = 0.003), the percentage of fat tissue (p = 0.009), and visceral fat (p = 0.021). Contrary to this, the serum adropin levels in female KTRs were associated with overall muscle mass and skeletal muscle mass , as shown in p = 0.019), while in women, this difference was noticed only in the percentage of body water (p = 0.039). As shown in p = 0.035), triglycerides (p = 0.037), and urate (p = 0.028) in the male group. Among female KTRs, a significant difference was detected only for potassium (p = 0.026).There was no statistically significant difference within the adropin subgroups in the time since transplantation, the type of dialysis, comorbidities, and smoking status. We observed significant differences in both sexes between the adropin subgroups for body weight (in women p = 0.022) and PhA (p = 0.025) were significant predictors of serum adropin levels in female KTRs.As presented in p < 0.001), BMI (p < 0.001), upper arm circumference (p = 0.010), and waist circumference (p = 0.010). Considering the body composition parameters, we found a significant negative association for most of the parameters for male KTRs. Male KTRs showed a significant negative association of adropin with the percentage of fat mass (p = 0.005), fat-free mass (p = 0.001), visceral fat (p = 0.021), muscle mass (p = 0.001), skeletal muscle mass, (p = 0.002), body water (p = 0.019), the percentage of body water (p = 0.015), PhA (p = 0.002), and trunk visceral fat (p = 0.002). When observing blood pressure parameters, the only significant and negative association was found for pDBP (p = 0.019) and cDBP (p = 0.041) in male KTRs, as shown in In contrast, in male KTRs, serum adropin levels were negatively associated with weight (p = 0.020) and a negative value with BMI (p < 0.001) as shown in The whole sample showed only a significant positive correlation of adropin values with age (p = 0.043) and PhA , whereas for male KTRs, the only significant negative predictors were BMI  and body water .After selecting the strongest predictors for serum adropin levels following the multivariate LASSO regression model, for female KTRs, the only significant negative predictors were skeletal muscle mass . However, KTRs are at risk of developing metabolic disorders. The increasing use of immunosuppressive therapy, which is potentially diabetogenic and atherogenic, can worsen pre-existing medical conditions or induce the development of other problems including bone disease, infections, and malignancies . The patSo far, there are several studies conducted on ESRD patients, which showed lower levels of adropin in patients receiving dialysis treatment compared to controls ,19. The The main strength of the study, to the best of our knowledge, is that this was the first research that investigated associations of serum adropin levels and body composition in an immunocompromised KTRs cohort. Furthermore, it is important to note that this KTRs cohort is specific for its low incidence of diabetes, obesity, and cardiovascular disease. It is important to underline that women in our cohort were all postmenopausal, which is another confirmation of the sex dependence of adropin in the KTRs population. The results of this research will provide new insights into the possible role of serum adropin in the pathophysiology of cardiovascular risk in these susceptible immunocompromised KTRs. The limitations of this study are a relatively small number of KTRs and a lack of healthy control. The cross-sectional study design did not allow for the definition of any causal relations.The present study shows that higher adropin levels are associated with different metabolic abnormalities in male KTRs compared to postmenopausal women. This confirms sex-dependent adropin differences in the immunocompromised KTRs population.


Dedifferentiated liposarcoma of the deep soft tissue of the lower extremities is an infrequent finding. Myxoid liposarcoma is considered the most common soft tissue neoplasia arising in this anatomic region. Divergent differentiation usually occurs within well-differentiated liposarcoma and is exceedingly rare in a myxoid liposarcoma. We report a 32-year-old man who developed a dedifferentiated liposarcoma of the thigh on the background of a pre-existing myxoid liposarcoma. The gross examination of the surgical specimen showed a 11/7/2 cm tumour mass with solid tan-grey areas and focal myxoid degeneration. The microscopic examination revealed a malignant lipogenic proliferation, containing round cells with hyperchromatic nuclei and atypical lipoblasts, confined to the basophilic stroma with a myxoid aspect. Abrupt transition towards a hypercellular, non-lipogenic area consisting of highly pleomorphic spindle cells with atypical mitotic figures was also noted. Immunohistochemical staining was performed. Tumour cells in the lipogenic area were intensely positive for S100 and p16, and CD34 staining highlighted an arborizing capillary network. The dedifferentiated tumour areas showed positive MDM2 and CDK4 staining within neoplastic cells, with the Ki 67 proliferation marker expressed in approximately 10% of the cells. Wild-type TP53 protein expression pattern was documented. Thus, the diagnosis of a dedifferentiated liposarcoma was established. This paper aims to provide further knowledge about liposarcomas with divergent differentiation at peculiar locations, emphasizing the importance of histopathologic examination and immunohistochemical analysis for establishing the diagnosis and assessing the therapeutic response and prognosis of this condition. Liposarcomas are rare malignant tumours which originate from the adipose tissue and can arise at any site, with a predilection for the lower extremities and retroperitoneum ,2. AccorOur patient was referred to the Emergency University Hospital in Bucharest where the CT scan indicated a dense tumour mass in the adipose tissue of the left thigh, showing nodular enhancing areas. This radiology aspect was strongly suggestive of a liposarcoma. Therefore, surgical “en bloc” removal of the tumour mass was carried out, and the specimen was submitted to our pathology department. The tissue samples were fixed with 10% neutrally buffered formalin and then processed via conventional histopathological methods, using paraffin embedding, sectioning and Haematoxylin–Eosin (HE) staining. Afterwards, the sections were deparaffinised in toluene and alcohol, washed in PBS , incubated with normal serum, and then incubated with primary antibodies overnight. Later, washing in carbonate buffer and developing in 3,3′-diaminobenzidine hydrochloride/hydrogen peroxide and nuclear counterstain with Meyer’s Haematoxylin were performed. The immunohistochemical markers used were S100 antibody 4C4.9 mouse monoclonal, Biocare, Ki 67 antibody SP6 rabbit monoclonal, Biocare, CD 34 antibody QBEnd/10 mouse monoclonal, Biocare, p16 INK4a antibody BC42, mouse monoclonal, Biocare, p53 antibody DO7 rabbit monoclonal, Biocare, MDM 2 antibody ZR258 rabbit monoclonal, ZETA and CDK4 antibody ZR 394, rabbit monoclonal, ZETA. Furthermore, using the PubMed academic research engine, we reviewed all English language cases published until 2022. We included all cases of soft tissue tumours occurring in the lower extremities diagnosed as dedifferentiated liposarcoma in patients of any age and sex with any type of therapeutic management and clinical outcome of the disease. We excluded all patients diagnosed with other soft tissue tumours or benign proliferations of the adipose tissue. Furthermore, we excluded the cases of tumours reported as sarcomas with uncertain histotype.We present a 32-year-old male patient with no significant medical history who was referred to our hospital with a slow growing, painless tumour mass located in the upper region of the left thigh. No chemotherapy or radiation therapy, nor a history of trauma, had been documented in the patient’s medical record. A CT scan was performed, revealing a 11 cm, dense tumour mass arising in the adipose tissue of the left thigh, showing nodular enhancing areas. Moreover, multiple ipsilateral adenopathies were identified. Upon further radiology investigations, no distant metastases were detected. The patient underwent a surgical procedure consisting of wide local excision of the tumour with resection margins and lymphadenectomy. Afterwards, the surgical specimens were submitted to our Pathology Department.The gross examination revealed a 11/7/2 cm well-circumscribed, multinodular tumour with a heterogeneous aspect on the cut surface. The lesion presented a course lobular contour and yellow-tan colour, firm consistency and small gelatinous areas, as well as focal nodular, solid zones.The histological examination showed a malignant mesenchymal proliferation with a lobular growth pattern, exhibiting round–oval and fusiform neoplastic cells with hyperchromatic nuclei and occasional nuclear pseudoinclusions, separated by a loose, lightly basophilic stroma, containing extracellular mucoid pools .Moreover, a prominent vasculature, formed by delicate arborizing capillaries with a plexiform arrangement, was identified. The tumour lobules were surrounded by a rim of atypical lipoblasts. The malignant tumour proliferation displayed abrupt transition towards a divergent component  consistiNo lympho-vascular tumour emboli or perineural invasion were detected. Four surgically excised inguinal lymph nodes were examined, with mild reactive histiocytosis and no tumour invasion. As the histopathologic features of the lesion was highly suggestive of a dedifferentiated liposarcoma, several additional ancillary tests were carried out. First, immunohistochemical analysis was used to determine the following tumour immunophenotype: neoplastic cells of the lipogenic tumour component presented intense S100 and p16 protein expression , while CBoth components of the divergent tumour proliferation were associated with a wild type TP53 expression pattern, and approximately 10% of the tumour cells expressed the Ki 67 proliferation marker .The expression of MDM2 and CDK 4 markers within the tumour cells of the dedifferentiated component eventually established the diagnosis of a dedifferentiated liposarcoma developed on the background of a myxoid liposarcoma .The patient did not present with any metastatic lesions at the time of diagnosis. Therefore, the tumour proliferation was categorized as a stage III A lesion (pT2 pN0 M0) according to the TNM evaluation system. Genetic testing was also recommended to confirm and complete the diagnosis. The patient was later referred to the oncology department for evaluation and initiation of an adequate therapeutic scheme. After a six-month follow-up, the patient is disease-free, with no evidence of recurrence or metastases.Dedifferentiated liposarcoma is a malignant proliferation of the adipose tissue which typically involves the retroperitoneal space and, infrequently, lower extremities, spermatic cord and structures of the head and neck ,9. DedifRegarding the challenging histopathologic features of some liposarcomas which may lead to a misinterpreted diagnosis, the question of heterologous differentiation and tumour metaplasia has also been put  HeteroloThe evaluation of the tumour immunophenotype can facilitate the differential diagnosis of lesions with similar histomorphology, and it is also relevant in predicting the therapeutic tumour response . First, Clinical behaviour, prognosis and therapeutic management of soft tissue liposarcoma has been analysed in several studies for a notably long time. To this moment, the prognosis of patients developing dedifferentiated liposarcoma of the extremities can be estimated considering multiple prognostic factors. First, dedifferentiated liposarcoma is regarded as a malignant tumour with a better prognosis compared to myxoid and pleomorphic liposarcoma and other soft tissue sarcomas, exploiting the fact that the majority of these adipocytic tumours harbour MDM2 and CDK4 expression ,7. CliniImmunohistochemical and genetic analyses of dedifferentiated liposarcoma and the other malignant tumours with adipocytic differentiation represent an important research purpose with important new perspectives. Surveys implying new correlations between molecular biology data, genetic analysis and immunohistochemistry have constantly been performed, in order to discover advanced tools for diagnosis and customized oncological treatment. For example, there is an interest in investigating the expression of GATA proteins and other molecules within various malignant tumours. Transcription factors from the GATA protein family are involved in adipose tissue development and maturation . Their cDedifferentiation within a myxoid liposarcoma is an exceedingly rare phenomenon, although cases of myxoid liposarcoma with heterologous differentiation, as well as dedifferentiated liposarcoma with a myxoid stroma have been reported so far. The precise diagnosis of dedifferentiated liposarcoma requires the use of ancillary studies, implying immunohistochemistry and genetic analysis. Differentiating the aforementioned malignant tumour from other soft tissue sarcomas is crucial, considering the significant differences regarding the prognosis and treatment of each entity. Therapeutic procedures have constantly been improved. Therefore, a diligent assessment of the tumour histotype, immunophenotype and genetic background is mandatory for the successful management of patients developing dedifferentiated liposarcomas.


Carbon stable isotope analysis is traditionally performed by bulk mass spectrometry. Although Raman spectroscopy distinguishes 12CO2 and 13CO2 isotopologue bands in spectra, using this technique to determine CO2 isotopic signature has been challenging. Here, we report on in-situ non-destructive analyses of the C stable isotopic composition of CO2, applying a novel high-resolution Raman configuration on 42 high-density CO2 fluid inclusions in mantle rocks from the Lake Tana region (Ethiopia) and El Hierro (Canary Islands). We collected two sets of three spectra with different acquisition times at high spectral resolution in each fluid inclusion. Among the 84 sets of spectra, 58 were characterised by integrated 13CO2/12CO2 band area ratios with reproducibility better than 4‰. Our results demonstrate the determination of δ13CCO2 by Raman spectroscopy in individual fluid inclusions with an error better than 2.5 ‰, which satisfactorily matches bulk mass spectrometry analyses in the same rock samples, supporting the accuracy of the measurements. We thus show that Raman Spectroscopy can provide a fundamental methodology for non-destructive, site-specific, and spatially resolved carbon isotope labelling at the microscale.Measuring the carbon stable isotope ratio ( This is because relative differences in 13CO2/12CO2 isotope ratios discriminate the different Earth carbon reservoirs and their mixtures owing to isotopic fractionation9. At crustal to mantle depths, geogenic CO2 can be trapped (and preserved) in fluid inclusions, micrometre-sized cavities in rock minerals containing micro- to pico-moles of fluid  notation14 and computed relative to an international measurement standard (the Vienna Pee Dee Belemnite (VPDB) standard) in per mil (δ13CCO2 ‰).Carbon stable isotope compositions of CO2 is traditionally measured by bulk analyses of volcanic gas and fluid inclusions in mineral and rock samples using mass spectrometry, which meets requirements of measurement accuracy and precision but is also technically challenging due to the small quantity of fluid extracted from the crushing or heating of samples, the relatively large amount of fluid sample required, and the specific sample preparation16.The carbon isotopic composition of Earth's deep CO13CCO2 with several advantages. First, it is possible to analyse in-situ 10–6–10–8 mol of CO2 in individual fluid inclusions of 10–20 µm in size or less20. Also, spectroscopic analyses are real-time, non-destructive and require minimal sample preparation, allowing for measuring carbon isotope signatures at the micrometre scale; such spatial resolution is impossible with bulk techniques.Raman micro-spectroscopy can also determine the δ13CO2 and 12CO2 isotopologue bands in spectra  of CO2 in individual fluid inclusions based on 13CO2/12CO2 band area ratios. These authors reported very low reproducibility due to instrumental factors. More recently, a new generation of high-resolution confocal Raman systems and detectors renewed interest in in-situ analyses of 13CO2/12CO2 ratios in fluid inclusions and optical cells33. This research improved the analytical potential of Raman micro-spectroscopy in determining the carbon stable isotope composition of CO2. However, none of these studies matched precision and/or accuracy adequately, raising the question of the applicability of this technique for C stable isotopes punctual analysis of CO2 fluids.Raman vibrational modes allow distinguishing the 2 fluid inclusions. We focused on high-density CO2 fluid inclusions in mantle rocks from two different localities—The Canary Islands and the Ethiopian plateau—to investigate the isotope signature of deep Earth fluids. We resolve analytical issues and present new carbon isotope ratios of CO2 in single fluid inclusions with an error better than 2.5‰ , providing a method to effectively measure the mass ratio of carbon isotopes at the micrometre scale in geological samples.The assumption of a linear relationship between the relative concentration of a molecular species and its contribution to the Raman spectrum is often not applicable. Instrumental drifts can induce subtle variations in the signal-to-noise ratio, affecting spectral output and, consequently, band intensity and shape. In order to reduce the intrinsic analytical errors related to the discrete nature of light photons , in this study, we developed experimental parameters for Raman measurements of stable carbon isotopes in individual CO2 were designed to be consistent with theoretical aspects of Raman spectroscopy. Quantitative Raman analyses in gas/fluid mixtures are based on Placzek's polarizability theory, which states that in a system of freely oriented molecules, the Raman scattering intensity depends on the number of scattering molecules within the system or analytical volume24. Thus, the Raman signal intensity is proportional to the component concentration according to:i is the Raman signal intensity of the gas component i, LI is the laser intensity, K represents spectroscopic and analytical factors , P is the optical path length, σi is the Raman scattering cross-section of component i, and Xi is the relative amount (mol %) of component i38.Experiments described here for C isotope ratio measurements of CO"real band intensity", defined as the product of the measured band intensity and full width at half maximum (FWHM), can reduce the uncertainties related to intensity count measurements34. This relation can be expressed with integrated band areas37, constraining the band intensity to the band shape in two-dimensional space37.As shown by Eq. , band ini, of single component i is proportional to its relative concentration, Xi 40 as follows:i, σi, and ζi are the integrated band area, the wavelength-dependent Raman scattering cross-section, and the instrumental efficiency for species i, respectively; An, σn, and ζn are the band areas, the wavelength-dependent Raman scattering cross-sections, and the instrumental sensitivity, respectively, for all n species within the system. Because there is no significant variation in bond energy between 13CO2 and 12CO2 isotopologue molecules, as evidenced by the very close band position, we assume equal Raman scattering factors for isotopically substituted molecules41. In addition, the instrument sensitivity does not vary in the measured interval from 1200 to 1400 cm−1  and 12CO2 lower band (ν2), respectively42, form the Fermi diad43; two hot bands flank the Fermi diad, at higher and lower wavenumbers, respectively compared to ν1 and ν2. A weak band to the left of the 12CO2 ν1 is the 13CO2 upper band (ν1)45. Because the heavier 13CO2 isotope is scarce compared to the more abundant 12CO2, the intensity and area of the 13CO2 band are about 102 times weaker than the 12CO2 band  and applying high confocality (100 µm pinhole) while using relatively short acquisition times . Long accumulation times—up to several hours—have been previously applied to enhance the intensity of the 13CO2 band, integrating the 13CO2 and the 12CO2 band areas measured in several consecutive accumulations31. However, extended accumulation times may induce spectral variations caused by instrumental drift, measurement errors or other artefacts, including fluorescence background and other sources of noise48 , orthopyroxene (Opx), and clinopyroxene (Cpx) from mantle rocks  and El Hierro Island , which are part of a collection at the Università Milano Bicocca  to increase the fluid mass in the analytical volume to a few micromoles  because these can modify the background, position, and shape of the CO2 bands52. Fluid inclusion data are summarised in Supplementary Table We analysed 42 COcks Fig. a,b from 2 fluid inclusions, we collected two consecutive sequences of three Raman spectra at the same focal point   . With this instrumental setting, 13CO2 and 12CO2 bands were collected in two separate spectra , and 1371 ± 0.03 and 1399 ± 0.03 cm−1 (12CO2 band). Following baseline correction, we performed band fitting by Pseudo-Voigtian curves to obtain the most accurate band position, intensity, shape, and area37. We adopted a Split Pseudo-Voigt algorithm53 as the best statistical interpolator of 12CO2 and 13CO2 raw bands, correcting the apparent asymmetries of the two bands by modifying curve parameters such as the shape of the base, the full width at half maximum (FWHM), and the intensity . In some spectra, the error of the fitting algorithms for the considerably lower intensity 13CO2 bands increases, resulting in under- or over-estimations of the integrated band areas . Results are shown in Fig. 13CO2/12CO2 band area ratios were very consistent, with maximum variations in band area ratios ranging from 0.000003 to 0.000046. Conversely, in twenty-six sets of spectra, at least one 13CO2/12CO2 band area ratio differed by one or two orders of magnitude from the other two ratios , calculated after Marshall et al.27:‰ range between 0.14 and 4.03‰, indicating excellent reproducibility , independent from fluid inclusion size, density and depth within the sample  of individual CO2 fluid inclusions based on the averaged integrated 13CO2/12CO2 area ratios by applying Eq. 13CCO2values for mantle rocks of Lake Tana region (Ethiopia) and El Hierro (Canary Islands) are reported in Table 13CCO2 in thirteen fluid inclusions from four distinct samples ranges from − 7.60 to − 5.53‰ (mean = − 6.73 ± 0.66‰). In orthopyroxene, δ13CCO2 in seven fluid inclusions from five distinct samples ranges from − 8.16 to − 6.53‰ (mean = − 7.34 ± 0.52‰). Both olivine and orthopyroxene fluid inclusions show a substantial homogeneity in the isotopic composition of carbon .We calculated the stable carbon isotopic composition δ1CCO2‰ of 13CCO2 in four fluid inclusions from three distinct samples ranges from 0.01 to 4.85‰ (mean = 2.40 ± 2.34‰). In orthopyroxene, δ13CCO2 in ten fluid inclusions from three distinct samples ranges from -1.89 to 1.45‰ (mean = − 0.20 ± 1.17‰). In clinopyroxene, δ13CCO2 in six fluid inclusions from two distinct samples ranges from − 2.12 to − 1.34‰ (mean = − 1.87 ± 0.27‰). There is a progressive decrease of δ13CCO2 values and relative standard deviations from olivine to orthopyroxene and clinopyroxene fluid inclusions, the latter showing the highest homogeneity in the measured ratios  the expected CO2 upper mantle range, overlapping the range of values reported for limestone (from − 1 to + 1‰2).In mantle rocks from the Canary Islands, olivine δ2 in individual fluid inclusions. The advantage of this method is that it is non-destructive and spatially resolved at the micron scale, which reveals a promising application prospect for the analysis of geogenic CO2 fluids. We succeeded in performing isotope measurements by developing a simple strategy for improving spectral analysis, thus reducing erratic analytical noise effects. High laser power, high confocality, and short acquisition times should ensure the optics' best efficiency to collect the highest signal-to-noise ratio to successfully investigate δ13CCO2 in a single CO2 fluid inclusion. With this novel analytical configuration, most analyses are characterised by high reproducibility (RC = 0.14–4.03‰), allowing us to calculate δ13CCO2 with 1σ better than 2.5‰. Noteworthy, δ13CCO2 determinations expressed as integrated and averaged  area ratios and the simultaneous collection of 13CO2 and 12CO2 scattering cancel most uncertainties related to instrumental performance without applying instrument corrections.The present results confirm that Raman microspectroscopy is reliable for measuring stable carbon isotopes of CO13CCO2 measurements by comparing present results with those obtained by isotope-ratio mass-spectrometry technique in the same rock samples. We point out that the latter technique implies that the obtained δ13CCO2 signature refers to the bulk of fluid inclusions hosted in the analysed crystals. Carbon stable isotope measurements by bulk mass spectrometry in minerals from four rock samples from the Canary Islands report δ13CCO2 values averaging 0.38‰ in Ol, − 1.74‰ in Opx and − 1.94‰ in Cpx (1σ =  ± 0.3‰55). As shown in Fig. 13CCO2 values reported in the different samples and minerals analysed with the Raman and conventional ratio mass spectrometric techniques. Notably, the inter-sample variability of the δ13C values is similar for both methods. Raman calculated δ13CCO2 values, although slightly heavier, fall in the same intervals . Nevertheless, Raman-based carbon stable isotope calculations are accurate and precise enough to record the slight variations of δ13CCO2‰ in the different minerals. To account for enriched 13C observed in mantle CO2 fluids from the Canary Islands, Sandoval-Velasquez et al.55 suggested a recycled crustal carbon component in the El Hierro mantle source, previously unidentified in volcanic gases/groundwater studies in the region. It is beyond the scope of this study to consider the scientific implications of the obtained results. Here, we focus on the reliability of the carbon isotopic measurements obtained in fluid inclusions of mantle rocks through the Raman technique. The present study suggests that Raman microspectroscopy can be an integrative method for δ13CCO2‰ determination in geological investigations. It gives a new perspective approach to push C isotopic measurement to the micrometre scale and enables applications such as tracing the origin of different CO2 fluid fluxes within the Earth.Similarly, in Opx , the average Raman δ2 Raman spectra have been collected on thick (100–150 µm) rock sections polished on both sides by the HORIBA LabRAM HR Evolution Raman System at the Dipartimento di Scienze dell'Ambiente e della Terra (DISAT), Università di Milano-Bicocca. The spectrometer system has an 800 mm focal distance and is coupled with an air-cooled 1024 × 256 px CCD detector cooled by Peltier effect (− 70 °C). Single point analyses have been performed using a linearly polarised solid-state green laser source at 532.06 nm with a nominal 300 mW output, powered at 150 mW by the 50% neutral density filter. Raman spectra acquisition was performed with a backscattered geometry by focusing the laser beam inside fluid inclusions to a maximum depth of 20 µm below the sample surface using a transmitted light Olympus BX41 microscope. A × 100 objective  with a long working distance was used for all the acquisitions to increase spatial resolution (≤ 1 µm3). The confocal pinhole was set at 100 µm diameter. The 1800 grooves per mm grating allow a spectral interval coverage from 1069.98 to 1522.70 cm−1 with a spectral per pixel resolution of about 0.44 cm−1/px. Accumulation times were varied from 30 s to 8 min to achieve suitable signal-to-noise enhancement. Each measurement was repeated thrice at the conditions for statistical analyses. Calibration was performed daily based on the auto-calibration process by the Raman system Service relative to the zero line and the silicon standard (520.7 cm−1), according to the ASTM 1840-96 normative59. The linearity of the spectrometer was also automatically checked and corrected during the process60. The spectrometer efficiency in the considered wavenumber region was checked with a white lamp of known emission intensity. A further considered point is the effect of temperature variation on grating dispersion and spectrometer focal length. Therefore, the temperature in the laboratory was pre-set at 20 °C and maintained constant within ± 0.5 °C.COSupplementary Information 1.Supplementary Tables.


Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression. A Bayesian state-dependent evolutionary phylodynamic model (SDevo) quantifies the difference in division rates between cells at the periphery and interior of a tumour. In simulations and using clinical hepatocellular carcinoma data, the authors use SDevo to interrogate spatial patterns in tumour growth. Tumours develop and progress via an evolutionary and ecological process wherein cellular sub-populations expand and diversify. Over the course of tumour development, tumour cells acquire genetic mutations and new phenotypes that potentially help them compete for resources and adapt for success in their microenvironment. Understanding this process is critical to predicting clinically important events such as if, how and when cells metastasize or develop resistance to therapy.7. Taken together, these effects may combine to create an environment in which cells on the boundary of a tumour have higher growth rates compared to those in the centre .Although tumour cell growth and success are often attributed to genetic and epigenetic aberrations, an additional important determinant of cell growth is physical location within the tumour. Position governs access to oxygen, nutrients, pro-growth signalling from the stroma, pH, cell–cell interactions and degree of immune exposure, all of which can affect cellular proliferation9, microbial experiments12, and decades of cancer computational and mathematical models18. Such investigations have revealed that boundary-driven growth blunts the efficacy of natural selection in selecting for beneficial  mutations and purging slower growing  lineages19. Boundary-driven growth should also enhance the effectiveness of adaptive therapy21 and cell–cell competition in the tumour interior. Further, such growth patterns should distort our expectations for the neutral variant allele frequency (VAF) spectrum22, which has been used as a null model for identifying signatures of natural selection23, and it has been qualitatively suggested in tumour simulation studies that boundary-driven growth could be misinterpreted as selection on tumour trees17. Therefore, establishing and incorporating these null expectations and models for boundary-driven tumour growth is essential in the context of the increasing interest in applying evolutionary theory to clinical disease, for example, in designing adaptive therapy24, identifying driver events26 or estimating timings of metastases27.Cancer biologists have long been interested in boundary-driven growth because it changes the evolutionary processes and genetic signatures of tumour progression. The evolutionary impact of boundary-driven growth has been explored via evolutionary theory28, and subsequent cell staining approaches that found markers of cell division cluster preferentially on the tumour periphery30. Similar patterns have been noted in cultured tumour spheroids32 and organoids34. Since then, analysis of both clinical samples—via immunohistochemistry36, spatial transcriptomics39 and genetic analysis41—and experimental systems, such as fluorescentlytracked xenografts44, have further supported spatial heterogeneity and preferential expansion on the tumour periphery in some tumours.An extensive history of clinical and experimental observations supports the importance of boundary-driven growth in tumour populations. These observations date back to the pioneering work of Thomlinson and Gray, which first identified necrotic structures with surrounding boundaries of growing cells from histological sections41 found that many colorectal tumours showed genetic patterns not consistent with boundary-driven growth, and a recent genetic analysis of renal cell carcinomas found the most recent common ancestors of metastatic lineages in the resected tumour interiors as opposed to the tumour boundaries45. Additionally, experimental evidence suggests that although centre-bound cells may experience oxygen and nutrient deprivation, hypoxia-related signalling can be linked to stem-cell-like tumour phenotypes with increased survival and chemotherapy resistance47. These observations highlight that higher proliferation on the tumour edge is not necessarily synonymous with long-term lineage survival and progression48.However, more recent studies have hinted at more complex modes of clinical tumour growth. One51, detecting parallel evolution of gene hits within a tumour53 and resolving whether metastases emerge early or late in tumour development55. In contrast, phylodynamic methods, which link shapes of phylogenetic trees to underlying population dynamics, have only rarely been used in cancer genomics56, despite widespread application in other fields58.A primary challenge in reconciling these conflicting observations is that clinical sequencing often captures only a limited snapshot of tumour diversity and growth. However, this sampled tumour diversity still offers a window into past population dynamics via phylogenetic and phylodynamic tools. Phylogenetic approaches, which reconstruct how cells within a tumour are related, have already proved useful in interrogating cancer evolution—for example, in determining the relative ordering of driver mutations61, in which cells have different birth and death rates on the tumour edge and centre, and lineages can transition between states as the tumour grows. Crucially, we introduce an extension that links cell birth and mutation, and therefore incorporates rates of sequence evolution that depend on each cell lineage’s inferred history of spatial locations . We provide this state-dependent evolution model (SDevo) as a package in the popular open-source Bayesian software BEAST 2 (ref. 62). We show that SDevo substantially improves our ability to infer boundary-driven growth dynamics in simulated tumours compared to non-cancer multi-type birth–death models, and validate this approach across a broad array of biological and sampling conditions, including those encompassing selection for driver mutations, three-dimensional (3D) growth and clinical sampling strategies. Finally, we apply SDevo to spatially resolved multi-region sequencing data from hepatocellular carcinomas (HCCs)40 and estimate that cells on the tumour boundary may have birth rates up to three to six times faster than those in the interior. More broadly, SDevo is a general tool for quantifying growth processes linked to any discrete state, and future investigations will expand beyond boundary-driven growth.Although phylodynamic approaches have high potential impact in cancer clinical settings, they are generally not adapted to study tumour biology or to incorporate the complexities of cancer’s spatial growth. To bridge this gap, we set out to develop a phylodynamic model suited for detecting boundary-driven growth in tumours. First, we quantify characteristic branching and genetic patterns in tumour trees simulated under boundary-driven growth, and demonstrate that these patterns correspond to cellular lineages spending different amounts of time on the faster-growing tumour edge versus in the tumour centre. To fully exploit these patterns for inference, we develop a novel phylodynamic tool based on the multi-type birth–death process64. Simulated tumours grow from single cells over discrete time steps and gain mutations at cell division. Under spatially constrained boundary-driven growth, a cell can only divide if there is an empty lattice spot in its Moore (eight-cell) neighbourhood, effectively tying its fitness to neighbourhood density  lattice, following a rich literature of studying cancer dynamics via Eden modelsWe first investigated how such growth processes affect the shape and structure of cancer phylogenetic trees to identify detectable tree signals of boundary-driven growth. We considered two types of tree representation: (1) time trees Fig. , where tIn Fig. 61, which ties differential rates of birth, death and sampling of lineages to multiple discrete states. In our simulation studies, we observe that boundary-driven growth can be effectively simplified into two states. We find that the instantaneous cell birth rate under boundary-driven growth is elevated only in cells immediately adjacent to the tumour edge, but is uniformly low in all cells in the interior . In other words, the most mutated cells have spent the majority of their lineage history on the tumour edge. Under unrestricted growth .As tree structures differ between tumours simulated under boundary-driven and unrestricted spatial constraints, we sought a phylodynamic approach that could differentiate between these two growth modes. One such model is the multi-type birth–death modelior Fig. . We can ior Fig. . If we twth Fig. , we obseTo investigate the robustness of these patterns, we next simulated tumours under a wide range of cell turnover rates. Under boundary-driven growth, increasing cell turnover decreases spatial constraints and therefore lessens the growth advantage between edge and centre states  and, importantly, may be biased by sampling procedures in clinical tumour biopsies. To address these shortcomings, we introduce the SDevo model to directly tie state-dependent birth rates to clock rates. This enables the model to combine information, both from mutation and branching patterns that arise from boundary-driven growth , SDevo is able to accurately quantify birth rate differences, whereas a two-state birth–death model without a state-dependent clock (estimated using BDMM-Prime and a strict clock model) fails , the strict clock multi-type birth–death model often fails to detect the same birth rate differences , we also implemented a random sampling scheme as might be present in single-cell studies . Although SDevo is general in scope and applicability, here we demonstrate that it successfully infers birth rate differences between the tumour edge and centre from multi-region sequencing data. We show that SDevo is relatively robust to sampling choices  and biological factors . We further find quantitative evidence for boundary-driven growth in clinically derived HCCs resected at a single time point.40. The authors originally hypothesized that Tumour 1’s tree structure matched a simulated scenario of boundary-driven growth followed by the expansion of a selected clone in the centre and that Tumour 2’s tree structure matched dominant boundary-driven growth. The authors made these assessments by simulating tumours and comparing the distributions of clones and variant allele frequencies to the sequenced tumours. They further noted that genetic divergence was higher in punches collected from the tumour periphery.Our assessment of boundary-driven growth in HCCs quantitatively expands the observations of ref. 71, highlight the importance of employing multiple tree patterns to quantify interacting modes of tumour growth. Although future work should more comprehensively profile how multiple spatial and non-spatial drivers of growth can impact observed tree patterns, our analysis of non-neutral tumours . Even more broadly, SDevo could be applied to study the growth impacts of other environmental or cell-intrinsic factors, for instance, immune invasion, hypoxia, metastatic versus primary sites or genetic features, by decomposing complex phenotypes into discrete states.Importantly, the utility of SDevo is not limited to understanding the impact of boundary-driven growth, but in fact can be applied in any instance in which sequenced tumour samples can be classified into discrete, observable states. Immediately, SDevo could be extended to test other proposed tumour growth modes—for example, growth against a solid surface, such as bone in osteosarcoma, along a unidirectional invasive front77 or other approaches that compare simulated and clinical tumours via summary statistics18. To be clear, these approaches have yielded extensive insights into tumour evolution, including patterns under boundary-driven growth41. However, these approaches are computationally costly, requiring the generation of often tens or hundreds of thousands of simulated tumours, on which one must compute extensive summary statistics. In addition, ABC comes with technical challenges, including the necessary choice  of low-dimensional sufficient summary statistics. Although Bayesian phylodynamics comes with its own technical challenges , it does not require tumour simulation. Furthermore, the generality of discrete traits affecting growth dynamics means it is easily adaptable to answer new questions. While both ABC and phylodynamics offer ways to understand clinically derived samples, the full promise of phylodynamics has yet to be widely exploited.Phylodynamic approaches such as SDevo have major advantages compared with our current approaches for estimating evolutionary information from tumours, namely approximate Bayesian computation (ABC)81, the expansion of tree models to permit differential birth rates could improve timing accuracy. (2) Incorporating differential growth rates across a tree can lead to more accurate tree topologies, as has been demonstrated in influenza evolving in multiple host species82. (3) Inferring ancestral states can elucidate population history and tumour evolutionary processes at time points that cannot be clinically sampled. A recent study45 analysed the intra-tumour spatial and genetic architecture of renal cancers and concluded cells in the tumour centre are more likely to seed metastasis. However, the study was limited to observing the extant position of these samples, whereas SDevo reconstructs these states at the time of clinical events . These three points suggest more broadly how tumour trees can be leveraged to gain new quantitative insights into tumour evolution, and demonstrate the broad utility of modelling evolutionary processes on trees.Phylodynamic approaches to understanding tumour evolution offer additional benefits. (1) Used in conjunction with well-calibrated molecular clocks, inferred trees can help estimate the timing of clinically important events, such as the emergence of subclones or metastatic events. While these analyses have been employed in the context of uniform growth rates65), can improve inference in cases where sampling may be non-uniform. This may be particularly important when sampling rates vary, for example, countries with variable rates of molecular surveillance for SARS-CoV-2. To facilitate broad application, SDevo is built as a package in the popular Bayesian phylogenetic platform BEAST 262. As with all phylodynamic models, identifiability represents a pervasive concern, but incorporating biological knowledge for determining priors can help constrain the model space. In our analysis of HCCs, we use information about cell transition and death rates to distinguish between multiple parameters that impact trees and estimation in interrelated ways.Beyond its application to cancer evolution, SDevo is a novel phylodynamic model with broad usefulness to incorporate state-dependent clock rates into evolutionary inference. While the field of phylogenetics has developed a broad array of clock models, to our knowledge, SDevo represents the first model in which clock rate is linked to birth rate. SDevo could be particularly useful in microbial and viral populations where diversification and mutational accumulation operate on similar timescales, and may be linked to underlying state variables . We demonstrated that incorporating clock rate differences, instead of relying solely on tree diversification rates  and a probability of attempting division given survival (λ). Under boundary-driven growth, cells only successfully divide if there is an empty lattice spot in their Moore neighbourhood. If multiple neighbouring spaces are available then a cell randomly chooses the location for its daughter cell from open neighbouring spaces. Under unrestricted growth, if a cell attempts division, its daughter cell will occupy an empty lattice spot in the Moore neighbourhood if available, but if not, the cell will still divide and push cells in a random direction to make space. Overlapping cells are pushed in the same direction until a neighbouring lattice spot is available, which the pushed cell will occupy. In both simulations, if a cell divides, each daughter cell can gain mutations with probability μ (per division per genome). Mutations are then drawn from a Jukes–Cantor model of sequence evolution and follow an infinite-sites assumption. Therefore, each time a mutation is gained, a site is added to all cells in the simulation. Simulations are stopped when the number of living cells is more than 1,000. The ground truth birth rates are assessed at discrete time points in the simulation by recording the current state of each cell and the proportion of cells that have progeny in the next time step. True birth rates are considered to be the mean across all time steps weighted by the number of cells in each category. This method calculates effective birth and death rates on the edge and centre given the simulated spatial constraints by calculating empirical division rates on the edge and centre of cells through simulated time. Effective spatial constraints in the boundary-driven model were controlled by changing the probability of cell death, where increased cell turnover allows centre-trapped cells to divide more readily  and a range of 0–0.35 per day (per cell) probability of death. Although clinical tumours have large variability in rates of proliferation, death and mutation, these parameters fit within this biological range94.An agent-based model was implemented in Python3, which places simulated cells on a 2D lattice. Simulations are initiated with a single cell in the centre of the lattice. At each time step (2 h) cells have a probability of dying ))−1. Cells die at rate d, regardless of their pressure status. To simulate selection, during each cell division, a daughter cell can acquire a driver mutation conferring a 10% fitness advantage95 with probability μdriver, which acts multiplicatively 96. Tumours are grown to a final size of N extant cells, of which n are sampled. After the simulation, a Poisson-distributed number of neutral mutations is augmented to each cell division with λ = μpassenger. Using the continuous space model, we investigated all pairwise combinations of 2D and 3D, neutral and selective scenarios, and ran 25 tumour simulations for each combination of parameters , μpassenger = 1, n = 100), except for 3D selection, where we simulated d =  with 10 tumours each. For the 2D models, N = 10, 000, and for the 3D model, N = 15, 000. For the selective model, μdriver = 0.01, and for the neutral models, μdriver = 0. Note, we used a value of μdriver well above expected rates of driver mutations (~10−5)92 to conservatively test SDevo in an extreme case of selection. To probe SDevo’s performance when cellular constraint is reduced by migration instead of cell death, we performed 10 simulations at d = 0.2, where cells migrate at 0, 0.5, 1, 1.5 or 2μm min−1 at an angle drawn from  and updated on average each minute . To probe SDevo’s robustness under a sigmoidal relationship between pressure and birth rate, we ran 10 simulations with d = , and all other parameters as above. One outlier in the 3D boundary-driven growth simulations was removed due to convergence on a local optimum. Ground truth edge and centre birth rates were determined by first classifying cells as within 10 μm (approximately 1 cell width) of the tumour periphery as edge, and those more than 10 μm from the edge as centre. The average birth rate was computed separately within each of those classes over multiple discrete time points  and combined by a weighted average according to the number of cells at each time point. Cells under too much pressure to divide at the sampled time (simple_pressure > τ) were calculated as having an instantaneous birth rate of 0.To probe the robustness of SDevo to more complex selective events and higher dimensions, we implemented an additional set of simulations in the physics-based cellular simulator, PhysiCellz-plane at 2/8, 3/8, 4/8, 5/8 and 6/8 of the range of the z values of a given tumour. Within each slice, cells were sampled to maximize the inter-cell distance, as described above, and the number of cells per slice was proportional to the number of cells in the slice relative to the number of cells across all slices.2D simulations were sampled by maximizing the distance between sampled single cells in physical space (diversified sampling). This ensures that a sufficient number of edge and centre classified cells were sampled and that sampled cells were not clustered. Bulk punch biopsy sampling was mimicked by choosing a centre cell and a target of eight cells immediately surrounding that were grouped into a single punch. Punches were iteratively drawn and shifted if they overlapped with a previously punched group of cells. Sampling ended when the target number of punches was reached (50 punches) or sampling was no longer possible without significant overlap. Punch sequences were generated using all mutations above a cellular fraction cutoff of 0.3. 3D sampling was approximated by taking five simulated slices through the tumour 97. The multi-type birth–death model extends this by considering birth, death and sampling to occur in different states  and how lineages jump between these states. The rates of birth, death and sampling vary depending on the state of a lineage. For the case of boundary-driven growth, we model a two-state process, with one state denoting cells in the centre of the tumour and the other state denoting cells on the edge of the tumour.The birth–death process describes how lineages duplicate (birth), die (death) and are sampled (where samples are tips on a phylogenetic tree)P of the timed phylogenetic tree T, the evolutionary model and parameters (σ), and the population model and parameters θ, given the data, D. This posterior probability is typically expressed as:D∣σ, T) and the tree prior P(T∣θ) to be independent, as the rate of evolution directly depends on the population model. In other words, how fast evolution happens on a lineage depends directly on the state of that lineage. We therefore define θ of the multi-type birth–death model. We then define the tree likelihood as P(T∣θ) directly, we only compute the tree prior for one realization of the state transition history, that is, To perform Bayesian inference, we define the posterior probability 61, which is achieved by treating the states of lineages probabilistically instead of discretely.Performing Markov chain Monte Carlo (MCMC) inference to characterize this posterior probability distribution would require integrating over all transition histories i in the phylogeny, its expected time spent in each state s. This leaves us with:Last, we set T spent in each state. To do so, we use a stochastic mapping approach related to those described in refs. 99. We first compute the probability i in the phylogenetic tree being in any possible state s over time t from the tips to the root (denoted with b for backwards in time) as described in ref. 61. These state probabilities are conditional only on events that occurred more recently than t and therefore not on all events in the phylogeny. During this backwards propagation, we keep track of the time-dependent transition matrix Q(t)i that describes the rate of probability flow between any two states at time t due to state transitions or birth events between states. As a result, once we reach the root, f of lineage i being in state s.In order to model different rates of evolution for different states, we first compute the expected time each lineage in the phylogenetic tree 100, we first define μab being the rate of state change due to state transitions or cross-birth events between states a and b.Following ref. We then compute the probabilities of any lineage being in any possible state conditional on all events in the phylogeny i spends in any of the possible states s. The values for cs is the rate of evolution, that is, the clock rate, of a lineage in state s. Next, we compute the average rate of evolution on branch i, ci, as:By keeping track of the forward probabilities equation ). We thebs in state s and the clock rate in state s are proportional such that c1 = cavgb1, c2 = cavgb2,…, cn = cavgbn.At each replication, an error in copying the genetic material of a cell can occur. These errors tend to be more likely in cancer cells, where cellular control mechanisms are often faulty. Phylogenetic methods typically assume the evolutionary processes to be independent of population processes, such as cell replication. To model mutations happening at birth events, we assume that the birth rate https://github.com/nicfel/SDevo). SDevo depends on BDMM-Prime v0.0.30 (https://github.com/tgvaughan/BDMM-Prime) to compute the tree prior P(T∣θ) and is built in Beast v2.6.6. To model mutations occurring at cell division, we set the relative rate of evolution in the different compartments (edge and centre) to be proportional to the birth rates in these compartments. The implementation itself does not explicitly require this assumption and the relative rates of evolution can also be treated as a distinct parameter in the inference. All SDevo analyses were performed using SDevo v0.0.2. SDevo can be installed through the interface BEAUti.We implemented the multi-type birth–death model with state-dependent clock rates as an addition to the Bayesian phylogenetics software BEAST 2  cases that converged to local optima. We summarized the output posterior distributions by mean and 95% HPD intervals. We further inferred maximum clade credibility (MCC) trees with median heights using BEAST 2.6.2 TreeAnnotator62. TreeAnnotator also gives posterior state probabilities for each MCC internal node.We applied SDevo to outputs of the Eden and PhysiCell simulations generated as described above. For each simulated tumour we calculated clock rate (mutations/tree length/sequence length) and edge and centre sampling rates . We set exponential priors on birth, death and transition rates. Full parameterization can be reproduced from XML templates. MCMC chains were run to convergence. We used only chains that had a minimum effective sample size for birth rate parameters greater than 200 for analysis. We also excluded rare . To reduce computational requirements and improve convergence, we generated input pseudo-sequences by randomly subsampling 25,000 variable sites. We summarized results across three independent subsamples for each tumour. We called presence or absence of a variant at each site based on a VAF cutoff of 0.05. VAF histograms displayed single-peaked distributions characteristic of a single major clone per sample, with the exception of tumour sample T1L13  and death rates , with S denoting the standard deviation of the log-transformed distributions. We used an exponential prior for the edge-to-centre transition rate (mean = 1). Note that these units are also arbitrary and are not calibrated to clinical time. In applying SDevo to these tumours, we constrain the parameter space in several ways to adapt to having relatively few samples, only a single observed time point and unknown sampling proportion. (1) We assume unidirectional transition so that cells can only move from edge to centre but not vice versa. As we only have a few observed state transition events, the transition rates would otherwise be relatively poorly informed. (2) We set priors on mean birth, death and transition rates across the two states. Birth and death priors are identical across both states, while transition rates priors are asymmetrical to inform unidirectional transition and enable convergence in a complex parameter space. Full parameterization can be found in the XML template. We combined posterior estimates across three independent runs for each tumour. We inferred MCC trees with ancestral state reconstructions with TreeAnnotator. In addition to the SDevo-inferred trees and parameters, we also generated maximum likelihood trees using FastTree102 and Augur103 under a Jukes–Cantor model for each tumour using all reported variable sites. Homoplastic sites contributed to lower support for one node in the maximum likelihood tree of Tumour 1 (Fig. We use a r 1 Fig.  and we mFurther information on research design is available in the Reporting Summary