2H MRI-based quantification of leucine uptake in glioblastoma multiforme

2H MRI-based quantification of leucine uptake in glioblastoma multiforme | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results 2 H MRI-based quantification of leucine uptake in glioblastoma multiforme Samantha McClendon , Xia Ge , Kyu-Ho Song , Dustin Grief , Shannon P. Fortin Ensign , View ORCID Profile Vikram D. Kodibagkar , Leland S. Hu , Joel R. Garbow , View ORCID Profile Scott C. Beeman doi: https://doi.org/10.1101/2025.04.23.648848 Samantha McClendon 1 School of Biological and Health Systems Engineering, Arizona State University , Tempe, AZ B.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xia Ge 2 Department of Radiology, Washington University , St Louis, MO Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kyu-Ho Song 2 Department of Radiology, Washington University , St Louis, MO Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dustin Grief 3 Department of Cancer Biology, Mayo Clinic , Scottsdale, AZ M.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shannon P. Fortin Ensign 4 Departments of Cancer Biology and Hematology and Oncology , Mayo Clinic, Scottsdale, AZ Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vikram D. Kodibagkar 1 School of Biological and Health Systems Engineering, Arizona State University , Tempe, AZ Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vikram D. Kodibagkar Leland S. Hu 5 Departments of Radiology, Cancer Biology, and Neurological Surgery, Mayo Clinic , Phoenix, AZ M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joel R. Garbow 6 Department of Radiology and the Alvin J. Siteman Cancer Center, Washington University , St Louis, MO Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Scott C. Beeman 1 School of Biological and Health Systems Engineering, Arizona State University , Tempe, AZ Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Scott C. Beeman For correspondence: scott.beeman{at}asu.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Glioblastoma (GBM) brain tumors are among the most lethal of all human cancers, with a median survival time of ∼15 months. Treatment planning requires radiologic demarcation of tumor boundaries with contrast-enhanced magnetic resonance imaging (CE MRI); however, significant tumor burden extends beyond the contrast-enhancing margins of the tumor. GBM tumors have an increased expression of amino acid (AA) transporters, including the Alanine, Serine, Cysteine Transporter 2 (ASCT2) and the L-Type Amino Acid Transporter 1 (LAT1). This upregulation has been leveraged in positron emission tomography (PET) studies to detect tumor burden beyond the contrast-enhancing margins identified by standard-of-care CE MRI. Here we leverage recent approaches in deuterium metabolic magnetic resonance with this known upregulation of AA transporters in GBM to demonstrate that 2 H MR can detect glioma based on enhanced branched- chain amino acid (BCAA) uptake. To the best of our knowledge, these data represent the first non- invasive quantification of AA concentrations in brain tumor and raises the potential to (i) detect tumor burden beyond contrast-enhancing margins and (ii) quantify AA metabolism using 2 H MR spectroscopy. 1. Introduction Glioblastoma (GBM) brain tumors are among the most aggressive and lethal of all human cancers, with a 5-year survival of less than 5% and median survival time of ∼15 months. 1 , 2 Standard-of- care surgery and radiation therapy rely on accurate detection and demarcation of these tumors, most commonly achieved via contrast-enhanced magnetic resonance imaging (CE MRI). CE MRI is the cornerstone of nearly all radiologic and clinical management of GBM and relies on the leakage of a gadolinium-based contrast agent through the permeable blood brain barrier (BBB) that is characteristic of tumor tissue. As this method is based on the extravasation of the agent from the vascular space into the interstitial space, it does not directly identify tumor cells, which often extend well beyond the boundaries of BBB disruption. 3 , 4 GBM cells are known to have reprogrammed metabolism of amino acids (AAs), which aids in their proliferation, signal transduction, adaptation, and survival. 5 Increased uptake of AAs is facilitated by upregulated expression of amino-acid transporters including the Alanine, Serine, Cysteine Transporter 2 (ASCT2) and the L-Type Amino Acid Transporter 1 (LAT1). 6 – 11 The known upregulation of AA transporters in GBM has enabled positron emission tomography (PET) studies to detect tumor based on increased uptake of radiolabeled AA tracers. Importantly, because AAs are actively transported directly into GBM cells, these radiotracers can identify tumor cells well beyond the boundaries of BBB disruption. 12 – 14 Many variations of AA-PET exist, most commonly using radiotracers O-(2-[18F]-fluoroethyl)-L-tyrosine (FET), 6–8,12–23,24(p11),25 L-methyl- 11C-methionine (MET), 6–8,12–23,24(p11),25 and 3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine (FDOPA). 6–8,12–23,24(p11),25 The branched-chain amino acid (BCAA) leucine-analog 18F- Fluciclovine, which is FDA-approved for imaging/detection of suspected prostate tumors, 26 has been used to image GBM in rodents and is positively correlated with tissue AA transporter levels. 27 However, AA-PET has major limitations in accessibility: (i) short half-lives of radio-labelled AA analogues necessitate imaging proximal to one of approximately 250 cyclotron facilities in the country, 28 typically limiting AA-PET access to major metropolitan regions; (ii) PET introduces clinical challenges in scheduling, time to diagnosis, and medical costs and, importantly, additional burden on patients who need to schedule a PET scan in addition to routine MRI; (iii) AA-PET tracers lack FDA approval for brain tumor imaging and thus remain non-reimbursable and inaccessible for most patients. Thus, most GBM patients never undergo this type of imaging. A method for detecting GBM cells based on AA uptake, but with accessibility like that of CE-MRI, would be a significant advance. Recent studies show that replacing protium with deuterium in metabolically active molecules such as glucose and acetate allows quantification of their uptake using 2 H metabolic magnetic resonance ( 2 H metabolic MR). 29 – 34 2H metabolic MR, including both 2 H imaging and 2 H single-voxel spectroscopy, is a method for measuring the uptake and metabolism of deuterium-enriched substrates. Among the many deuterated substrates incorporated into 2 H metabolic MR experiments are glucose, acetate, choline, fumarate, methionine, water, and recently, alanine. 35 , 36 Deuterated glucose (D-[6,6 ’ - 2 H 2 ] glucose) has been used extensively to study bacterial metabolism, 37 retinal metabolism, 38 , 39 glycogen synthesis, 40 red blood cell metabolism, 41 and, notably, cancer metabolism as related to the Warburg effect. 29 , 42 Motivated by advances in AA-PET and 2 H metabolic MR, we test the hypothesis that enhanced uptake of deuterated AAs in GBM can be quantified with 2 H MR and demonstrate this concept, which we refer to as 2 H AA-MR, in a rodent model. The success of this proof-of-concept study lays the foundation for a novel, accessible tool to directly image GBM cells in the brain independent of BBB disruption. 2. Methods In vivo tumor and d 10 -leucine dose preparation All animal experiments were approved by the Washington University Institutional Animal Care and Use Committee. Three tumor-bearing mice were prepared by stereotactically injecting 50k GL261 cells into the dorsal striatum of 70-day-old female C57BL/6 mice. Tumors were allowed to grow for approximately 20 days, reaching volumes of 58, 49, and 46 mm 3 , respectively. A 100 mM d 10 -leucine solution was prepared by adding DL- leucine-d 10 (98 atom % D) (Sigma-Aldrich, Co., St. Louis, MO) to deionized water and sonicating in an ultrasonic bath until dissolved. Note that in a separate experiment without an imaging component, 2 H leucine doses of up to 70.6 mg/100 g, were found to be safe in mice, with four female, 10-month-old, C57BL/6 mice surviving a 1 mL dose of 200 mM leucine (data not shown). Time course 2 H MR Spectroscopy (MRS) MRS was completed for three tumor-bearing and three control mice using a home-built volume transmit, surface receive coil configuration on a 11.7 T Varian system. For each animal, two pre-injection, natural-abundance 2 H Spin-Echo full-Intensity Acquired Localized (SPECIAL) spectra (5 minutes each, TR/TE = 450/4.27 ms) 43 were collected prior to 2 H leucine administration. Next, a target dose of 500 µL of 100 mM d 10 -leucine solution was injected via a subcutaneous catheter between the shoulder blades of the dorsal surface region (total dose 35.3 mg/100g) while the animal was in the scanner. Immediately following injection, 2 H SPECIAL spectra were collected in 5-minute increments for 70 mins post injection: TR/TE = 450/4.27 ms. Voxels were chosen to maximize volume within tumoral regions (3.3 × 4.9 × 4 mm 3 , 4.2 × 3.7 × 5 mm 3 , 4 × 4 × 4 mm 3 ). For healthy animals, a 3 × 6 × 5 mm 3 voxel was placed within the brain to maximize SNR. Cell culture and extraction GBM patient-derived xenograft (PDX) cell line GBM22 was provided by the Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource, Phoenix, AZ, USA. 44 Cells were first plated in DMEM 10% FBS and then serum starved (DMEM 0.1% BSA) for 12 hours. Next, cells were cultured in either DMEM 5% FBS or DMEM 5% FBS supplemented with 25 mM DL-Leucine-d 10 (98 atom % D) (Sigma-Aldrich, Co., St. Louis, MO) for 24 hours. Cells were harvested and rinsed twice with PBS to ensure removal of external media prior to extraction. Metabolites were extracted by a modified acetonitrile extraction protocol, 45 with basic steps as follows: (i) Cell pellets of ∼10 million cells were homogenized in a solution of acetonitrile:isopropanol:water (volume 3:3:2), (ii) Resulting cell extract and debris were centrifuged at 10,000 xg and 4 °C for 30 minutes, (iii) Recovered supernatants were roto- evaporated to dryness, (iv) Dried samples were reconstituted in a solution of acetonitrile: water (volume 1:1), then centrifuged and roto-evaporated again, and (v) Dried samples were reconstituted in 200 µL deionized water (Advanced Biomatrix, Inc. MS) and transferred to 3 mm tubes for NMR. 2 H-NMR spectroscopy 2 H NMR was performed on a Bruker Advance III NMR console at 9.4 T. The broadband channel of a Bruker 5 mm VSP NMR probe ([ 109 Ag- 31 P]/ 13 C/ 1 H with 2 H lock and Z gradient) was tuned to 2 H, at a resonant frequency of 61.42 MHz, for direct detection of the NMR signal. Spectra were collected with a 90° pulse, relaxation delay of 1s, acquisition time of 2s (recycling time of 3s), and sweep width of 3076.92 Hz. 5088 scans were acquired for each experiment. Phantom Preparation A d 10 -leucine phantom of 20 mM was created in a 1.5 mL centrifuge tube to measure relaxation time constants. The phantom was prepared by dissolving DL-Leucine-d 10 (98 atom % D) (Sigma-Aldrich, Co., St. Louis, MO) in a 2% low gelling temperature agarose solution (Sigma-Aldrich, Co., St. Louis, MO) heated on a hot plate, with D 2 O added to enhance the 4.7 ppm resonance. The sample was transferred to a 1.5 mL centrifuge tube and allowed to cool until gelled. T1 and T2 quantification Longitudinal and transverse relaxation time constants (T1 and T2, respectively) were measured using the d 10 -leucine gel phantom on a Varian 11.7 T small animal MRI system with a custom-built 2 H coil. T1 values of the HOD resonance and (-CD 3 ) 2 , -CD 2 , gamma-D, and alpha-D resonances of d 10 -leucine were measured with a two-pulse inversion- recovery experiment. Forty exponentially-spaced inversion times ranging from 0.02 to 2 s were used, with a TR of 2500 ms. A Carr-Purcell-Meiboom-Gill (CPMG) spin echo train experiment was then used to quantify the T2 values of the same resonances; 40 exponentially-spaced echo times ranging from 0.005 to 0.75 s were used, with a TR of 2500 ms. Data analysis MRS frequencies, amplitudes, and relaxation time constants were estimated using the Bayesian Analysis Toolbox, a data analysis package developed at Washington University in St. Louis. 46 , 47 Frequencies and decay rate/time constants across all animals and spectra in the time series were estimated jointly (i.e., all frequencies and decay rate constants are assumed to be the same across animals and points in the MRS time series), and all amplitudes across time points and animals were estimated independently. Deuterated leucine concentrations were calculated by referencing (-CD 3 ) 2 peak amplitudes to natural-abundance HOD (average of two 5-min 2 H SPECIAL scans, referenced to a concentration value of 16.35 mM 48 ) and adjusting for T1, T2, and stoichiometry/label loss. 49 , 50 Deuterated leucine concentrations from cell extraction experiments were calculated likewise, referencing the (-CD 3 ) 2 peak amplitude to a natural abundance HOD concentration of 15.37 mM, as measured and calculated for the deionized water solvent used for NMR (Advanced Biomatrix, Inc. MS) (see NMR experiment in Supplemental Figure 1). For relaxation experiments, T1 and T2 time constants were quantified by fitting a standard three-parameter (mono-exponential plus a constant) model to the data. Statistics For in vivo data, a Student’s t-test was used to compare deuterated leucine concentrations for the tumoral and control groups at the 70-minute time point ( α = 0.05). 3. Results In vivo 2 H AA-MR In vivo experiments support the feasibility of 2 H AA-MR for quantifying uptake of AAs in GBM. Enhanced uptake of d 10 -leucine in tumor tissue was observed over a 70- minute time course ( Figure 1(a) ), with ∼4-fold enhancement (p = 0.001, based on 70-minute time point) compared with comparable tissue in control animals observed at the 70-minute time point and a greater than 4-fold increase over the time course, compared to natural abundance 2 H leucine (conservatively estimated to be at the level of the noise floor) ( Figure 1(b) ).Tumoral concentrations of 2.0 mM ± 0.3 mM (at 70 min.) were achieved using doses of 35.5 mg/100 g, while essentially no uptake was seen in normal-appearing brain. Leucine concentrations in healthy brain were largely below the noise floor (around 0.5 mM; note that data points beneath the noise floor reflect the Bayesian probability software’s best estimate but are not deemed quantifiable by the authors). Assuming an approximate linear uptake of d 10 -leucine over the first 45 minutes of data collection, we estimate an uptake rate of approximately 2.8 μg/min. A corresponding plot of HOD concentrations shows no difference between tumor and control (p = 0.77, based on 70-minute time point) and an increase to approximately 1.5 times natural abundance HOD over the 70-minute time course in both tumoral and control animals (p < 0.001, Figure 1(c) ). Download figure Open in new tab Figure 1. (a) SPECIAL 2 H MR spectra of voxels representing tumor and control tissue (see voxel placement in (b)) over 70 minutes post-subcutaneous injection (5-minute intervals, lb = 10 Hz) show an increase in the leucine-associated peak at ∼0.7 ppm. (b) Concentrations of 2 H leucine over the 70-minute time course, calculated from (CD 3 ) 2 peak amplitudes referenced to the natural- abundance HOD peak. (c) Concentrations of HOD over the 70-minute time course, again referenced to the natural-abundance HOD peak. For each plot, light grey lines represent individual animals, while bolded lines represent averages across animals. Concentrations below the noise floor are shaded in grey. 2 H NMR of cell extracts The spectra generated from d 10 -leucine-exposed cells show 2 H leucine-associated resonances: (-CD 3 ) 2 at 0.78 ppm, and (-CD 2 ) and γ-D at 1.5 ppm. Given the extraction procedure used here (see Methods), we measure a 2 H leucine concentration of 0.45 mM for these extracts. In contrast, control spectra from cells not exposed to d 10 -leucine but undergoing the same extraction procedure do not exhibit any leucine-associated resonances above the noise floor but instead only exhibit the expected natural-abundance HOD resonance, referenced at 4.7 ppm ( Figure 2 ). Download figure Open in new tab Figure 2. Comparison of 2 H NMR spectra (5088 scans) of GBM 22 cell extracts from d 10 -leucine- exposed and non-exposed cells, showing leucine-associated (-CD 3 ) 2 resonance (0.78 ppm) and (- CD 2 ) and γ-D resonance (1.5 ppm) for 2 H-leucine-exposed cells. The natural-abundance HOD resonance is referenced to 4.7 ppm. In vitro relaxation time constant (T1/T2) measurements Measured T1 and T2 relaxation time constants were used in calculations of d 10 -leucine concentrations. Inversion recovery and CPMG experiments were performed using a 20 mM d 10 -leucine phantom and standard (three-parameter) mono-exponential-plus-a-constant models were fit to the data to determine T1 and T2 time constants. Leucine-associated resonances and their T1 and T2 relaxation time constants at a concentration of 20 mM were: (-CD 3 ) 2 T1/T2 = 166/148 ms, (-CD 2 ) and γ-D 117/60 ms, α-D 134 /46 ms. For the HOD peak, T1/T2 = 393/40 ms ( Figure 3 ). Download figure Open in new tab Figure 3. SPECIAL 2 H MR spectrum (4 × 4 × 4 mm 3 voxel) of a ∼20 mM deuterated leucine phantom in 2% agarose obtained at 11.7 T, plotted with 10 Hz line broadening. (The HOD resonance was enhanced with addition of D 2 O to the phantom.) Measured T1 and T2 values are tabulated in the insert in the upper left of the figure. 4. Discussion This study provides proof-of-concept data to support a 2 H AA-MR method to quantify the preferential uptake of AAs in GBM vs normal brain. We show a ∼6-fold enhancement in 2 H leucine concentrations in tumoral compared to healthy tissue following a subcutaneous injection of the agent; tumor concentrations of upwards of ∼2 mM were achieved using doses of 35.5 mg/100 g. Notably, 2 H NMR of cell extract experiments, in which we exposed cultured GBM cells to 2 H leucine and used 2 H NMR to quantify intracellular leucine concentration after washing cells and extracting metabolites, provide direct evidence of cellular leucine uptake independent of vascular leakage. This idea is further supported by the similarity in HOD time-series profiles between tumoral and healthy tissue ( Figure 1(c) ). HOD is produced via systemic metabolism of injected 2 H leucine and, being freely diffusible, enters the blood stream and distributes through the body in a way that can be approximated as a slow infusion of HOD (a similar delivery mechanism to the subcutaneous delivery of 2 H leucine). The similarity in the HOD time-series profiles between tumoral and healthy tissue suggests that delivery mechanisms that are not cell membrane transporter mediated are similar between tumor and healthy tissue. Given our in vitro data and these control HOD time-series data, we thus conclude that the difference between tumoral and healthy brain 2 H leucine concentrations is likely mediated by transporter-mediated cellular uptake of 2 H leucine into tumor cells. This idea of direct AA uptake is also well supported by extensive literature supporting the transporter-facilitated affinity of GBM to circulating leucine. 6 – 11 While further exploration is necessary to: (i) understand the extent of uptake versus possible extravasation of 2 H leucine and (ii) resolve the effects of differing cell types on degree of uptake, the leucine uptake confirmed by these 2 H NMR experiments confirms the potential for direct 2 H leucine uptake in GBM in vivo . Beyond 2 H AA uptake, the topic of this study, the 2 H AA-MR method may provide an opportunity to explore AA metabolic activity in brain tumors. This future direction could be a significant development in tumor diagnosis and staging, as abnormal AA metabolism is a critical biomarker of GBM malignancy. For example, enhanced GBM uptake and catabolism of BCAAs to lactate have been shown to drive GBM cell proliferation and are associated with a particularly aggressive hypoxic GBM phenotype. 10 , 11 , 51 By mimicking 2 H metabolic MR studies that track the appearance of glutamine/glutamate and lactate peaks in 2 H NMR spectra following administration of deuterated glucose, we might quantify the downstream appearance of known leucine metabolites such as glutamate/glutamine, lactate, or acetoacetate. However, doing so in vivo would require the ability to detect signal from peaks associated with lower 2 H concentrations than that of the (CD 3 ) 2 peak of d 10 -leucine here, as downstream metabolites would: (i) have fewer potentially deuterated functional groups, (ii) have probabilistically fewer deuterons in those locations, and (iii) potentially be more transitory in nature. In this regard, it must be noted that dramatic improvements in SNR have been made in the short period since the introduction of 2 H metabolic MR that have not been leveraged in this study. For example, Peters, et al., have demonstrated a 2 H multi-echo balance steady-state free-precession (ME-bSSFP) that improved SNR by a factor of ∼2-5 (depending on condition and spectrum peak). 52 Expanding on this work, Montrazi, et al., recently demonstrated a weighted average 2 H chemical shift imaging steady-state free precession (CSI- SSFP) sequence that increases SNR by a factor of ∼5 over the already-improved 2 H ME-bSSFP, for a total 10-25x improvement over the original 2 H CSI sequence used by De Feyter, et al 29 , 53 and the 2 H NMR experiments used herein. Additionally, in a separate study not reported here, the use of higher 2 H leucine doses of up to 70.6 mg/100 g were found to be safe in mice (see Methods), potentially doubling the 2 H leucine signal available in future studies. Combined, these methods could provide a theoretical 20-50 fold increase in detectability, and even if the gains are more modest, significant improvement in signal is expected. In this proof-of-principle study, we report an uptake rate assuming linear uptake, a rough but reasonable approximation based on the data. This lays a foundation for future studies focused on appropriate kinetic modelling, accounting for blood concentration and potential downstream metabolic conversion, as demonstrated for example in D-[6,6 ’ - 2 H 2 ] glucose 2 H metabolic MR. 54 While subcutaneous injections were used here as a surrogate for constant 2 H leucine administration to the bloodstream, preliminary data using IP and IV administration of the deuterated leucine produce comparable results (Supplemental Figure 2), suggesting consistency across dosing schemes. IV administration may allow for more control of dose timing and blood substrate levels in dynamic uptake and clearance studies. 55 A noninvasive and accessible method for quantifying AA uptake would be a significant clinical advance. Primarily, 2 H AA-MR provides an opportunity for imaging GBM directly based on enhanced expression of AA transporters, rather than BBB disruption. Practically, this method may readily translate to the clinic. Although x-nuclei capabilities are at present more commonly used in academic hospital settings, modification of clinical MRI systems to provide x-nuclei capabilities can be accomplished on-site with appropriate radiofrequency coils, filters, and radiofrequency amplifiers and pre-amplifiers. 56 PET is currently more commonly available in the clinic; however, these 2 H MRI modifications are highly affordable compared to the installation of PET imaging and cyclotron facilities. Contemporary coil design also allows for simultaneous 1 H and 2 H scanning, further reducing scan time per patient. Simultaneous 1 H/ 2 H scanning would fit well into a clinical workflow at many sites while eliminating the significant scheduling, cost, and quality- of-life burden of PET imaging. 5. Conclusions Herein, we present the first proof-of-principle confirming that 2 H MR can be used to quantify enhanced AA uptake by GBM. This 2 H MR method has the potential to significantly advance accurate and accessible imaging of these aggressive, infiltrative brain tumors based directly on AA uptake into GBM cells, rather than BBB breakdown. Moreover, the preliminary success of this method provides a foundation for the study of differential AA metabolism in GBM. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgements Grant acknowledgements: The research presented was partly supported by National Institutes of Health grant R01-EB032376, Arizona Biomedical Research Commission grant RFGA2022-010, and the ASU-Mayo Alliance Seed Grant. Special thanks to Joseph Ackerman (Washington University, St. Louis, MO) for his insights during experimental planning and continued valuable feedback throughout the course of this project. Funding National Institutes of Health, https://ror.org/01cwqze88 , R01-EB032376 Arizona Biomedical Research Commission, , RFGA2022-010 ASU-Mayo Alliance Seed Grant, , List of Abbreviations 2 H AA-MR Deuterium amino acid metabolic resonance AA Amino acid ASCT2 Alanine, Serine, Cysteine Transporter 2 BBB Blood brain barrier BCAA Branched-chain amino acid CE MRI Contrast enhanced magnetic resonance imaging FDOPA 3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine FET O-(2-[18F]-fluoroethyl)-L-tyrosine GBM Glioblastoma multiforme LAT1 L-Type Amino Acid Transporter 1 MET L-methyl-11C-methionine PET Positron emission tomography SPECIAL 2 H Spin-Echo full-Intensity Acquired Localized References 1. ↵ De Vleeschouwer S Tamimi AF , Juweid M. Epidemiology and Outcome of Glioblastoma . In: De Vleeschouwer S , ed. Glioblastoma . Codon Publications ; 2017 . Accessed March 5, 2024 . http://www.ncbi.nlm.nih.gov/books/NBK470003/ 2. ↵ Stupp R , Mason WP , van den Bent MJ , et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma . N Engl J Med . 2005 ; 352 ( 10 ): 987 – 996 . doi: 10.1056/NEJMoa043330 OpenUrl CrossRef PubMed Web of Science 3. ↵ Langen KJ , Galldiks N , Hattingen E , Shah NJ . Advances in neuro-oncology imaging . Nat Rev Neurol . 2017 ; 13 ( 5 ): 279 – 289 . doi: 10.1038/nrneurol.2017.44 OpenUrl CrossRef PubMed 4. ↵ Hu LS , Hawkins-Daarud A , Wang L , Li J , Swanson KR . Imaging of intratumoral heterogeneity in high-grade glioma . Cancer Lett . 2020 ; 477 : 97 – 106 . doi: 10.1016/j.canlet.2020.02.025 OpenUrl CrossRef PubMed 5. ↵ Chen S , Jiang J , Shen A , et al. Rewired Metabolism of Amino Acids and Its Roles in Glioma Pathology . Metabolites . 2022 ; 12 ( 10 ): 918 . doi: 10.3390/metabo12100918 OpenUrl CrossRef PubMed 6. ↵ Grosu AL , Astner ST , Riedel E , et al. An Interindividual Comparison of O-(2-[18F]Fluoroethyl)-L-Tyrosine (FET)– and L-[Methyl-11C]Methionine (MET)–PET in Patients With Brain Gliomas and Metastases . Int J Radiat Oncol . 2011 ; 81 ( 4 ): 1049 – 1058 . doi: 10.1016/j.ijrobp.2010.07.002 OpenUrl CrossRef PubMed 7. Habermeier A , Graf J , Sandhöfer BF , Boissel JP , Roesch F , Closs EI . System L amino acid transporter LAT1 accumulates O-(2-fluoroethyl)-L-tyrosine (FET) . Amino Acids . 2015 ; 47 ( 2 ): 335 – 344 . doi: 10.1007/s00726-014-1863-3 OpenUrl CrossRef PubMed 8. Okubo S , Zhen HN , Kawai N , Nishiyama Y , Haba R , Tamiya T. Correlation of l-methyl-11C-methionine (MET) uptake with l-type amino acid transporter 1 in human gliomas . J Neurooncol . 2010 ; 99 ( 2 ): 217 – 225 . doi: 10.1007/s11060-010-0117-9 OpenUrl CrossRef PubMed 9. Salisbury T , Arthur S. The Regulation and Function of the L-Type Amino Acid Transporter 1 (LAT1) in Cancer . Int J Mol Sci . 2018 ; 19 ( 8 ): 2373 . doi: 10.3390/ijms19082373 OpenUrl CrossRef PubMed 10. ↵ Suh EH , Hackett EP , Wynn RM , et al. In vivo assessment of increased oxidation of branched-chain amino acids in glioblastoma . Sci Rep . 2019 ; 9 ( 1 ): 340 . doi: 10.1038/s41598-018-37390-0 OpenUrl CrossRef PubMed 11. ↵ Tönjes M , Barbus S , Park YJ , et al. BCAT1 promotes cell proliferation through amino acid catabolism in gliomas carrying wild-type IDH1 . Nat Med . 2013 ; 19 ( 7 ): 901 – 908 . doi: 10.1038/nm.3217 OpenUrl CrossRef PubMed 12. ↵ Rapp M , Heinzel A , Galldiks N , et al. Diagnostic Performance of 18 F-FET PET in Newly Diagnosed Cerebral Lesions Suggestive of Glioma . J Nucl Med . 2013 ; 54 ( 2 ): 229 – 235 . doi: 10.2967/jnumed.112.109603 OpenUrl Abstract / FREE Full Text 13. ↵ Rapp M , Floeth FW , Felsberg J , et al. Clinical value of O-(2-[18F]-fluoroethyl)-L-tyrosine positron emission tomography in patients with low-grade glioma . Neurosurg Focus . 2013 ; 34 ( 2 ): E3 . doi: 10.3171/2012.12.FOCUS12336 OpenUrl CrossRef PubMed 14. ↵ Galldiks N , Stoffels G , Filss C , et al. The use of dynamic O-(2-18F-fluoroethyl)-L-tyrosine PET in the diagnosis of patients with progressive and recurrent glioma . Neuro-Oncol . Published online May 24, 2015:nov088. doi: 10.1093/neuonc/nov088 OpenUrl CrossRef PubMed 15. Dunet V , Rossier C , Buck A , Stupp R , Prior JO . Performance of 18 F-Fluoro-Ethyl-Tyrosine ( 18 F-FET) PET for the Differential Diagnosis of Primary Brain Tumor: A Systematic Review and Metaanalysis . J Nucl Med . 2012 ; 53 ( 2 ): 207 – 214 . doi: 10.2967/jnumed.111.096859 OpenUrl Abstract / FREE Full Text 16. Galldiks N , Langen KJ . Amino Acid PET – An Imaging Option to Identify Treatment Response, Posttherapeutic Effects, and Tumor Recurrence? Front Neurol . 2016 ; 7 . doi: 10.3389/fneur.2016.00120 OpenUrl CrossRef PubMed 17. Cicone F , Filss CP , Minniti G , et al. Volumetric assessment of recurrent or progressive gliomas: comparison between F-DOPA PET and perfusion-weighted MRI . Eur J Nucl Med Mol Imaging . 2015 ; 42 ( 6 ): 905 – 915 . doi: 10.1007/s00259-015-3018-5 OpenUrl CrossRef PubMed 18. ↵ Moulin-Romsée G , D’Hondt E , De Groot T , et al. Non-invasive grading of brain tumours using dynamic amino acid PET imaging : does it work for 11C-Methionine? Eur J Nucl Med Mol Imaging . 2007 ; 34 ( 12 ): 2082 – 2087 . doi: 10.1007/s00259-007-0557-4 OpenUrl CrossRef PubMed Web of Science 19. Weckesser M , Langen KJ , Rickert CH , et al. O-(2-[18F]fluorethyl)-L-tyrosine PET in the clinical evaluation of primary brain tumours . Eur J Nucl Med Mol Imaging . 2005 ; 32 ( 4 ): 422 – 429 . doi: 10.1007/s00259-004-1705-8 OpenUrl CrossRef PubMed Web of Science 20. Pöpperl G , Kreth FW , Mehrkens JH , et al. FET PET for the evaluation of untreated gliomas: correlation of FET uptake and uptake kinetics with tumour grading . Eur J Nucl Med Mol Imaging . 2007 ; 34 ( 12 ): 1933 – 1942 . doi: 10.1007/s00259-007-0534-y OpenUrl CrossRef PubMed Web of Science 21. Calcagni ML , Galli G , Giordano A , et al. Dynamic O-(2-[18F]fluoroethyl)-L-tyrosine (F-18 FET) PET for Glioma Grading: Assessment of Individual Probability of Malignancy . Clin Nucl Med . 2011 ; 36 ( 10 ): 841 – 847 . doi: 10.1097/RLU.0b013e3182291b40 OpenUrl CrossRef PubMed 22. Kratochwil C , Combs SE , Leotta K , et al. Intra-individual comparison of 18F-FET and 18F-DOPA in PET imaging of recurrent brain tumors . Neuro-Oncol . 2014 ; 16 ( 3 ): 434 – 440 . doi: 10.1093/neuonc/not199 OpenUrl CrossRef PubMed 23. Becherer A , Karanikas G , Szabó M , et al. Brain tumour imaging with PET: a comparison between [18F]fluorodopa and [11C]methionine . Eur J Nucl Med Mol Imaging . 2003 ; 30 ( 11 ): 1561 – 1567 . doi: 10.1007/s00259-003-1259-1 OpenUrl CrossRef PubMed 24. Youland RS , Kitange GJ , Peterson TE , et al. The role of LAT1 in 18F-DOPA uptake in malignant gliomas . J Neurooncol . 2013 ; 111 ( 1 ): 11 – 18 . doi: 10.1007/s11060-012-0986-1 OpenUrl CrossRef PubMed 25. Singhal T , Narayanan TK , Jain V , Mukherjee J , Mantil J. 11C-L-methionine positron emission tomography in the clinical management of cerebral gliomas . Mol Imaging Biol . 2008 ; 10 ( 1 ): 1 – 18 . doi: 10.1007/s11307-007-0115-2 OpenUrl CrossRef PubMed 26. ↵ Parent EE , Schuster DM . Update on 18 F-Fluciclovine PET for Prostate Cancer Imaging . J Nucl Med . 2018 ; 59 ( 5 ): 733 – 739 . doi: 10.2967/jnumed.117.204032 OpenUrl Abstract / FREE Full Text 27. ↵ Scarpelli ML , Healey DR , Mehta S , Quarles CC . Imaging Glioblastoma With 18F-Fluciclovine Amino Acid Positron Emission Tomography . Front Oncol . 2022 ; 12 : 829050 . doi: 10.3389/fonc.2022.829050 OpenUrl CrossRef PubMed 28. ↵ Cyclotrons used for Radionuclide Production . IAEA Accelerator Knowledge Portal . https://nucleus.iaea.org/sites/accelerators/Pages/Cyclotron.aspx 29. ↵ De Feyter HM , Behar KL , Corbin ZA , et al. Deuterium metabolic imaging (DMI) for MRI-based 3D mapping of metabolism in vivo . Sci Adv . 2018 ; 4 ( 8 ): eaat7314 . doi: 10.1126/sciadv.aat7314 OpenUrl FREE Full Text 30. Kreis F , Wright AJ , Hesse F , Fala M , Hu DE , Brindle KM . Measuring Tumor Glycolytic Flux in Vivo by Using Fast Deuterium MRI . Radiology . 2020 ; 294 ( 2 ): 289 – 296 . doi: 10.1148/radiol.2019191242 OpenUrl CrossRef PubMed 31. Morze C , Engelbach JA , Blazey T , et al. Comparison of hyperpolarized 13 C and non-hyperpolarized deuterium MRI approaches for imaging cerebral glucose metabolism at 4.7 T . Magn Reson Med . 2021 ; 85 ( 4 ): 1795 – 1804 . doi: 10.1002/mrm.28612 OpenUrl CrossRef PubMed 32. Ouwerkerk R. Deuterium MR Spectroscopy: A New Way to Image Glycolytic Flux Rates . Radiology . 2020 ; 294 ( 2 ): 297 – 298 . doi: 10.1148/radiol.2019192024 OpenUrl CrossRef PubMed 33. Riis-Vestergaard MJ , Laustsen C , Mariager CØ , Schulte RF , Pedersen SB , Richelsen B. Glucose metabolism in brown adipose tissue determined by deuterium metabolic imaging in rats . Int J Obes . 2020 ; 44 ( 6 ): 1417 – 1427 . doi: 10.1038/s41366-020-0533-7 OpenUrl CrossRef 34. ↵ Graaf RA , Hendriks AD , Klomp DWJ , et al. On the magnetic field dependence of deuterium metabolic imaging . NMR Biomed . 2020 ; 33 ( 3 ). doi: 10.1002/nbm.4235 OpenUrl CrossRef PubMed 35. ↵ Polvoy I , Qin H , Flavell RR , et al. Deuterium Metabolic Imaging—Rediscovery of a Spectroscopic Tool . Metabolites . 2021 ; 11 ( 9 ): 570 . doi: 10.3390/metabo11090570 OpenUrl CrossRef PubMed 36. ↵ Fisler M , Cohen D , Allouche-Arnon H , Montrazi E , Harris T , Bar-Shir A. Deuterated D-alanine: A Stable Tracer for In Vivo Cancer Imaging with 2H MRI . Poster presented at: European Molecular Imaging Meeting ; 2025 . 37. ↵ Aguayo JB , Gamcsik MP , Dick JD . High resolution deuterium NMR studies of bacterial metabolism . J Biol Chem . 1988 ; 263 ( 36 ): 19552 – 19557 . OpenUrl Abstract / FREE Full Text 38. ↵ Aguayo JB , McLennan IJ , Graham C , Cheng HM . Dynamic monitoring of corneal carbohydrate metabolism using high-resolution deuterium NMR spectroscopy . Exp Eye Res . 1988 ; 47 ( 2 ): 337 – 343 . doi: 10.1016/0014-4835(88)90016-4 OpenUrl CrossRef PubMed 39. ↵ Berkowitz BA , Garner MH , Wilson CA , Corbett RJ . Nondestructive measurement of retinal glucose transport and consumption in vivo using NMR spectroscopy . J Neurochem . 1995 ; 64 ( 5 ): 2325 – 2331 . doi: 10.1046/j.1471-4159.1995.64052325.x OpenUrl CrossRef PubMed Web of Science 40. ↵ Goodman MN , Masuoka LK , deRopp JS , Jones AD . Use of deuterium labelled glucose in evaluating the pathway of hepatic glycogen synthesis . Biochem Biophys Res Commun . 1989 ; 159 ( 2 ): 522 – 527 . doi: 10.1016/0006-291x(89)90024-7 OpenUrl CrossRef PubMed Web of Science 41. ↵ Hotchkiss RS , Song SK , Ling CS , Ackerman JJ , Karl IE . Sepsis does not alter red blood cell glucose metabolism or Na+ concentration: a 2H-, 23Na-NMR study . Am J Physiol . 1990 ; 258 ( 1 Pt 2 ): R21 – 31 . doi: 10.1152/ajpregu.1990.258.1.R21 OpenUrl CrossRef 42. ↵ Chang MC , Malut VR , Mahar R , et al. Assessing cancer therapeutic efficacy in vivo using [ 2 H7]glucose deuterium metabolic imaging . Sci Adv . 2025 ; 11 ( 13 ): eadr0568 . doi: 10.1126/sciadv.adr0568 OpenUrl CrossRef PubMed 43. ↵ Mlynárik V , Gambarota G , Frenkel H , Gruetter R. Localized short-echo-time proton MR spectroscopy with full signal-intensity acquisition . Magn Reson Med . 2006 ; 56 ( 5 ): 965 – 970 . doi: 10.1002/mrm.21043 OpenUrl CrossRef PubMed 44. ↵ Vaubel RA , Tian S , Remonde D , et al. Genomic and Phenotypic Characterization of a Broad Panel of Patient-Derived Xenografts Reflects the Diversity of Glioblastoma . Clin Cancer Res . 2020 ; 26 ( 5 ): 1094 – 1104 . doi: 10.1158/1078-0432.CCR-19-0909 OpenUrl Abstract / FREE Full Text 45. ↵ Fiehn O. Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling . Curr Protoc Mol Biol . 2016 ; 114 : 30 .4.1-30.4.32. doi: 10.1002/0471142727.mb3004s114 OpenUrl CrossRef 46. ↵ Quirk JD , Bretthorst GL , Garbow JR , Ackerman JJH . Magnetic resonance data modeling: The Bayesian analysis toolbox . Concepts Magn Reson Part A . 2018 ; 47A ( 2 ): e21467 . doi: 10.1002/cmr.a.21467 OpenUrl CrossRef 47. ↵ Kotyk JJ , Hoffman NG , Hutton WC , Bretthorst LG , Ackerman JJH . Comparison of Fourier and Bayesian Analysis of NMR Signals. I. Well-Separated Resonances (The Single-Frequency Case) . J Magn Reson . 1992 ; 98 : 483 – 500 . OpenUrl Web of Science 48. ↵ Ge X , Song KH , Engelbach JA , et al. Distinguishing Tumor Admixed in a Radiation Necrosis (RN) Background: 1H and 2H MR With a Novel Mouse Brain-Tumor/RN Model . Front Oncol . 2022 ; 12 : 885480 . doi: 10.3389/fonc.2022.885480 OpenUrl CrossRef PubMed 49. ↵ De Graaf RA , Thomas MA , Behar KL , De Feyter HM . Characterization of Kinetic Isotope Effects and Label Loss in Deuterium-Based Isotopic Labeling Studies . ACS Chem Neurosci . 2021 ; 12 ( 1 ): 234 – 243 . doi: 10.1021/acschemneuro.0c00711 OpenUrl CrossRef PubMed 50. ↵ Soni ND , Swain A , Jacobs P , et al. In vivo assessment of β-hydroxybutyrate metabolism in mouse brain using deuterium ( 2 H) MRS . Magn Reson Med . 2023 ; 90 ( 1 ): 259 – 269 . doi: 10.1002/mrm.29648 OpenUrl CrossRef PubMed 51. ↵ Zhang B , Chen Y , Shi X , et al. Regulation of branched-chain amino acid metabolism by hypoxia-inducible factor in glioblastoma . Cell Mol Life Sci . 2021 ; 78 ( 1 ): 195 – 206 . doi: 10.1007/s00018-020-03483-1 OpenUrl CrossRef PubMed 52. ↵ Peters DC , Markovic S , Bao Q , et al. Improving deuterium metabolic imaging (DMI) signal-to-noise ratio by spectroscopic multi-echo bSSFP: A pancreatic cancer investigation . Magn Reson Med . 2021 ; 86 ( 5 ): 2604 – 2617 . doi: 10.1002/mrm.28906 OpenUrl CrossRef PubMed 53. ↵ Montrazi ET , Sasson K , Agemy L , et al. High-sensitivity deuterium metabolic MRI differentiates acute pancreatitis from pancreatic cancers in murine models . Sci Rep . 2023 ; 13 ( 1 ): 19998 . doi: 10.1038/s41598-023-47301-7 OpenUrl CrossRef PubMed 54. ↵ Lu M , Zhu XH , Zhang Y , Mateescu G , Chen W. Quantitative assessment of brain glucose metabolic rates using in vivo deuterium magnetic resonance spectroscopy . J Cereb Blood Flow Metab Off J Int Soc Cereb Blood Flow Metab . 2017 ; 37 ( 11 ): 3518 – 3530 . doi: 10.1177/0271678X17706444 OpenUrl CrossRef PubMed 55. ↵ Song K , Ge X , Engelbach JA , et al. Subcutaneous deuterated substrate administration in mice: An alternative to tail vein infusion . Magn Reson Med . 2024 ; 91 ( 2 ): 681 – 686 . doi: 10.1002/mrm.29888 OpenUrl CrossRef PubMed 56. ↵ Hu R , Kleimaier D , Malzacher M , Hoesl MAU , Paschke NK , Schad LR . X-nuclei imaging: Current state, technical challenges, and future directions . J Magn Reson Imaging . 2020 ; 51 ( 2 ): 355 – 376 . doi: 10.1002/jmri.26780 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted April 24, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following 2H MRI-based quantification of leucine uptake in glioblastoma multiforme Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share 2 H MRI-based quantification of leucine uptake in glioblastoma multiforme Samantha McClendon , Xia Ge , Kyu-Ho Song , Dustin Grief , Shannon P. Fortin Ensign , Vikram D. Kodibagkar , Leland S. Hu , Joel R. Garbow , Scott C. Beeman bioRxiv 2025.04.23.648848; doi: https://doi.org/10.1101/2025.04.23.648848 Share This Article: Copy Citation Tools 2 H MRI-based quantification of leucine uptake in glioblastoma multiforme Samantha McClendon , Xia Ge , Kyu-Ho Song , Dustin Grief , Shannon P. Fortin Ensign , Vikram D. Kodibagkar , Leland S. Hu , Joel R. Garbow , Scott C. Beeman bioRxiv 2025.04.23.648848; doi: https://doi.org/10.1101/2025.04.23.648848 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cancer Biology Subject Areas All Articles Animal Behavior and Cognition (7642) Biochemistry (17715) Bioengineering (13907) Bioinformatics (42005) Biophysics (21472) Cancer Biology (18624) Cell Biology (25534) Clinical Trials (138) Developmental Biology (13391) Ecology (19935) Epidemiology (2067) Evolutionary Biology (24356) Genetics (15617) Genomics (22529) Immunology (17753) Microbiology (40437) Molecular Biology (17200) Neuroscience (88697) Paleontology (667) Pathology (2840) Pharmacology and Toxicology (4829) Physiology (7653) Plant Biology (15171) Scientific Communication and Education (2046) Synthetic Biology (4304) Systems Biology (9827) Zoology (2272)