Small RNA Sequencing across Diverse Biofluids Identifies Optimal Methods for exRNA Isolation.

Poor reproducibility within and across studies arising from lack of knowledge regarding the performance of extracellular RNA (exRNA) isolation methods has hindered progress in the exRNA field. A systematic comparison of 10 exRNA isolation methods across 5 biofluids revealed marked differences in the complexity and reproducibility of the resulting small RNA-seq profiles. The relative efficiency with which each method accessed different exRNA carrier subclasses was determined by estimating the proportions of extracellular vesicle (EV)-, ribonucleoprotein (RNP)-, and high-density lipoprotein (HDL)-specific miRNA signatures in each profile. An interactive web-based application (miRDaR) was developed to help investigators select the optimal exRNA isolation method for their studies. miRDar provides comparative statistics for all expressed miRNAs or a selected subset of miRNAs in the desired biofluid for each exRNA isolation method and returns a ranked list of exRNA isolation methods prioritized by complexity, expression level, and reproducibility. These results will improve reproducibility and stimulate further progress in exRNA biomarker development.

exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids.

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

Population-based heteropolymer design to mimic protein mixtures.

Biological fluids, the most complex blends, have compositions that constantly vary and cannot be molecularly defined1. Despite these uncertainties, proteins fluctuate, fold, function and evolve as programmed2-4. We propose that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters5,6; synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. Here, we extracted the chemical characteristics and sequential arrangement along a protein chain at the segmental level from natural protein libraries and used the information to design heteropolymer ensembles as mixtures of disordered, partially folded and folded proteins. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration, enhancing the thermal stability of proteins and behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials and, ultimately, realize matter-to-life transformations.

MS-Based Proteomics of Body Fluids: The End of the Beginning.

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification, and quantification, making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In in a previous review in 2017, we described technological and conceptual limitations that had held back success. We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification, and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. Shorter gradients, new scan modes, and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multiprotein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Quantitative detection of hydrogen peroxide in rain, air, exhaled breath, and biological fluids by NMR spectroscopy.

Hydrogen peroxide (H2O2) plays a key role in environmental chemistry, biology, and medicine. H2O2 concentrations typically are 6 to 10 orders of magnitude lower than that of water, making its quantitative detection challenging. We demonstrate that optimized NMR spectroscopy allows direct, interference-free, quantitative measurements of H2O2 down to submicromolar levels in a wide range of fluids, ranging from exhaled breath and air condensate to rain, blood, urine, and saliva. NMR measurements confirm the previously reported spontaneous generation of H2O2 in microdroplets that form when condensing water vapor on a hydrophobic surface, which can interfere with atmospheric H2O2 measurements. Its antimicrobial activity and strong seasonal variation speculatively could be linked to the seasonality of respiratory viral diseases.

Combining Thermal Desorption with Selected Ion Flow Tube Mass Spectrometry for Analyses of Breath Volatile Organic Compounds.

An instrument integrating thermal desorption (TD) to selected ion flow tube mass spectrometry (SIFT-MS) is presented, and its application to analyze volatile organic compounds (VOCs) in human breath is demonstrated for the first time. The rationale behind this development is the need to analyze breath samples in large-scale multicenter clinical projects involving thousands of patients recruited in different hospitals. Following adapted guidelines for validating analytical techniques, we developed and validated a targeted analytical method for 21 compounds of diverse chemical class, chosen for their clinical and biological relevance. Validation has been carried out by two independent laboratories, using calibration standards and real breath samples from healthy volunteers. The merging of SIFT-MS and TD integrates the rapid analytical capabilities of SIFT-MS with the capacity to collect breath samples across multiple hospitals. Thanks to these features, the novel instrument has the potential to be easily employed in clinical practice.

Degradation Behavior of Medical MgZZC-1 in Various Simulated Body Fluids.

Magnesium-based biodegradable metal bone implants exhibit superior mechanical properties compared to biodegradable polymers for orthopedic and cardiovascular stents. In this study, MgZZC-x (x = 1, 1.2) alloys were screened by in vitro biocompatibility tests in three simulated body fluids under nontoxic conditions. The MgZZC-1 alloys with better biocompatibility were selected to predict the days required for complete degradation. The evolution of degradation products was analyzed, and the mechanism of formation of the product film was inferred. A degradation kinetic model was established to investigate the effect of MEM components on the degradation of the alloys. The results demonstrate that the proteins in MEM can greatly retard the degradation progress by attaching to the surface of MgZZC-1 alloys, which are predicted to degrade completely within 341 days. The carbonate and phosphate buffers were adjusted to pH in MEM solution, delaying the degradation of magnesium alloys. This process in MEM more accurately reflects the actual degradation in the body and is superior to that in Hanks and SBF solutions. This study will promote the application of biodegradable materials in clinical medicine.

(Un)Reliable detection of menstrual blood in forensic casework - evaluation of the Seratec® PMB test with mock samples.

The identification of the type of body fluid in crime scene evidence may be crucial, so that the efforts are high to reduce the complexity of these analyses and to minimize time and costs. Reliable immunochromatographic rapid tests for specific and sensitive identification of blood, saliva, urine and sperm secretions are already routinely used in forensic genetics. The recently introduced Seratec® PMB test is said to detect not only hemoglobin, but also differentiate menstrual blood from other secretions containing blood (cells) by detecting D-dimers. In our experimental set-up, menstrual blood could be reliably detected in mock forensic samples. Here, the result was independent of sample age and extraction buffer volume. It was also successfully demonstrated that all secretions without blood cells were negative for both, hemoglobin (P) and D-dimer (M). However, several blood cell-containing secretions/tissues comprising blood (injury), nasal blood, postmortem blood and wound crust also demonstrated positive results for D-dimer (M) and were therefore false positives. For blood (injury) and nasal blood, this result was reproduced for different extraction buffer volumes. The results of this study clearly demonstrate that the Seratec® PMB test is neither useful nor suitable for use in forensic genetics because of the great risk of false positive results which can lead to false conclusions, especially in sexual offense or violent acts.

Advances in body fluid identification: MiRNA markers as powerful tool.

Body fluids are one of the most encountered types of evidence in any crime and are commonly used for identifying a person's identity. In addition to these, they are also useful in ascertaining the nature of crime by determining the ty pe of fluid such as blood, semen, saliva, urine etc. Body fluids collected from crime scenes are mostly found in degraded, trace amounts and/or mixed with other fluids. However, the existing immunological and enzyme-based methods used for differentiating these fluids show limited specificity and sensitivity in such cases. To overcome these challenges, a new method utilizing microRNA expression of the body fluids has been proposed. This method is believed to be non-destructive as well as sensitive in nature and researches have shown promising results for highly degraded samples as well. This systematic review focuses on and explores the use and reliability of miRNAs in body fluid identification. It also summarizes the researches conducted on various aspects of miRNA in terms of body fluid examination in forensic investigations.

A portable EIS-based biosensor for the detection of microcystin-LR residues in environmental water bodies and simulated body fluids.

Due to the eutrophication of water bodies around the world, there is a drastic increase in harmful cyanobacterial blooms leading to contamination of water bodies with cyanotoxins. Chronic exposure to cyanotoxins such as microcystin leads to oxidative stress, inflammation, and liver damage, and potentially to liver cancer. We developed a novel and easy-to-use electrochemical impedance spectroscopy-based immunosensor by fabricating stencil-printed conductive carbon-based interdigitated microelectrodes and immobilising them with cysteamine-capped gold nanoparticles embedded in polyaniline. It has been also coupled with a custom handheld device enabling regular on-site assessment, especially in resource-constrained situations encountered in developing countries. The sensor is able to detect microcystin-LR up to 0.1 µg L-1, having a linear response between 0.1 and 100 µg L-1 in lake and river water and in serum and urine samples. In addition to being inexpensive, easy to fabricate, and sensitive, it also has very good selectivity.

Sample Matrices for Mass Spectrometry-Based Adherence Monitoring: A Systematic Critical Review.

BACKGROUND: Analytical monitoring of adherence using mass spectrometry (MS) plays an important role in clinical toxicology. Unambiguous detection of drugs (of abuse) and/or their metabolites in body fluids is needed to monitor intake of medication as prescribed or to monitor abstinence as a follow-up to detoxification procedures. This study focused on the advantages and disadvantages of different sample matrices used for MS-based adherence monitoring. METHODS: Relevant articles were identified through a literature search in the PubMed database. English articles published between January 01, 2017, and December 31, 2022, were selected using the keywords "adherence assess*" or "adherence monit*" or "compliance assess*" or "compliance monit*" in combination with "mass spectrom*" in the title or abstract. RESULTS: A total of 51 articles were identified, 37 of which were within the scope of this study. MS-based monitoring was shown to improve patient adherence to prescribed drugs. However, MS analysis may not be able to assess whether treatment was rigorously followed beyond the last few days before the sampling event, except when hair is the sample matrix. For medication adherence monitoring, blood-based analyses may be preferred because reference plasma concentrations are usually available, whereas for abstinence control, urine and hair samples have the advantage of extended detection windows compared with blood. Alternative sample matrices, such as dried blood samples, oral fluid, and exhaled breath, are suitable for at-home sampling; however, little information is available regarding the pharmacokinetics and reference ranges of drug (of abuse) concentrations. CONCLUSIONS: Each sample matrix has strengths and weaknesses, and no single sample matrix can be considered the gold standard for monitoring adherence. It is important to have sufficient information regarding the pharmacokinetics of target substances to select a sample matrix in accordance with the desired purpose.

exoRBase 2.0: an atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids.

Extracellular vesicles (EVs) are small membranous vesicles that contain an abundant cargo of different RNA species with specialized functions and clinical implications. Here, we introduce an updated online database (http://www.exoRBase.org), exoRBase 2.0, which is a repository of EV long RNAs (termed exLRs) derived from RNA-seq data analyses of diverse human body fluids. In exoRBase 2.0, the number of exLRs has increased to 19 643 messenger RNAs (mRNAs), 15 645 long non-coding RNAs (lncRNAs) and 79 084 circular RNAs (circRNAs) obtained from ∼1000 human blood, urine, cerebrospinal fluid (CSF) and bile samples. Importantly, exoRBase 2.0 not only integrates and compares exLR expression profiles but also visualizes the pathway-level functional changes and the heterogeneity of origins of circulating EVs in the context of different physiological and pathological conditions. Our database provides an attractive platform for the identification of novel exLR signatures from human biofluids that will aid in the discovery of new circulating biomarkers to improve disease diagnosis and therapy.

Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples.

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.

Unveiling Morphine: A Rapid and Selective Fluorescence Sensor for Forensic and Medical Analysis.

Opioid use, particularly morphine, is linked to CNS-related disorders, comorbidities, and premature death. Morphine, a widely abused opioid, poses a significant global health threat and serves as a key metabolite in various opioids. Here, we present a turn-off fluorescent sensor capable of detecting morphine with exceptional sensitivity and speed in various samples. The fluorescent sensor was developed through the dimerization process of 7-methoxy-1-tetralone and subsequent demethylation to produce the final product. Despite morphine possessing inherent fluorophoric properties and emitting light in an approximately similar wavelength as the sensor's fluorescent blue light, the introduction of the target molecule (morphine) in the presence of the sensor caused a reduction in the sensor's fluorescence intensity, which is attributable to the formation of the sensor-morphine complex. By utilizing this fluorescence quenching sensor, the chemo-selective detection of morphine becomes highly feasible, encompassing a linear range from 0.008 to 40 ppm with an impressive limit of detection of 8 ppb. Consequently, this molecular probe demonstrates a successful application in determining trace amounts of morphine within urine, yielding satisfactory analytical results. The study also explores the effect of several variables on the sensor's response and optimizes the detection of morphine in urine using a response surface methodology with a central composite design.

Assessment of Blood and Semen Detection and DNA Collection from Swabs up to Three Months after Deposition on Five Different Cloth Materials.

Body fluid identification plays a crucial role in criminal investigations. Because of their presence in many cases, blood and semen are the most relevant body fluids in forensic sciences. Based on antigen-antibody reactions binding unique proteins for each body fluid, serological assays represent one of the most rapid and highly specific tests for blood and semen. Currently, few studies have assessed the factors affecting body fluid identification by applying these assays. This work aimed to study the effect of different fabrics from clothes and time since deposition on identification through immunochromatographic tests for blood and semen, DNA isolation, and STR profiling from these samples. Body fluids were deposited on black- and white-dyed denim and cotton fabrics, and on leather. Afterward, blood and semen were sampled at 1 day, 30 days, and 90 days after deposition and identified by using the SERATEC® HemDirect Hemoglobin Test and the PSA Semiquant and SERATEC® BLOOD CS and SEMEN CS tests, respectively. Laboratory and crime scene tests presented similar performances for the detection of blood and semen stains on every tested fabric. No differences were found on band intensities between timepoints for all fabrics. It was possible to recover and identify blood and semen samples up to three months after deposition and to obtain full STR profiles from all the tested fabrics. Both body fluid STR profiles showed differences in their quality between 1 and 90 days after deposition for all fabrics except for black cotton for semen samples. Future research will expand the results, assessing body fluid identification on other substrates and under different environmental conditions.

ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention.

The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.

Development of Rapid Bioactivity-Expressed Zr-50Ti Alloys by Surface Treatment with Modified Simulated Body Fluid.

Zr-50Ti alloys are promising biomaterials due to their excellent mechanical properties and low magnetic susceptibility. However, Zr-50Ti alloys do not inherently bond well with bone. This study aims to enhance the bioactivity and bonding strength of Zr-50Ti alloys for orthopedic implant materials. Initially, the surface of Zr-50Ti alloys was treated with a sulfuric acid solution to create a microporous structure, increasing surface roughness and area. Subsequently, low crystalline calcium phosphate (L-CaP) precipitation was controlled by adding Mg2+ and/or CO32- ions in modified simulated body fluid (m-SBF). The treated Zr-50Ti alloys were then subjected to cold isostatic pressing to force m-SBF into the micropores, followed by incubation to allow L-CaP formation. The apatite-forming process was tested in simulated body fluid (SBF). The results demonstrated that the incorporation of Mg2+ and/or CO32- ions enabled the L-CaP to cover the entire surface of Zr-50Ti alloys within only one day. After short-term soaking in SBF, the L-CaP layer, modulated by Mg2+ and/or CO32- ions, formed a uniform hydroxyapatite (HA) coating on the surface of the Zr-50Ti alloys, showing potential for optimized bone integration. After soaking in SBF for 14 days, the bonding strength between the apatite layer and alloy has the potential to meet the orthopedic application requirement of 22 MPa. This study demonstrates an effective method to enhance the bioactivity and bonding strength of Zr-50Ti alloys for orthopedic applications.

Predicting nitrogen excretion of cattle kept under tropical and subtropical conditions using semimechanistic models.

The present study aims at evaluating whether current semimechanistic models developed for temperate cattle systems can be adopted for cattle under (sub-) tropical husbandry systems to adequately (accurately and precisely) predict total nitrogen (TN), urine nitrogen (UN), faecal nitrogen (FN) excretion and its partition into different FN fractions. Selected models were built based on the feeding recommendations for ruminants of the British (Model A), German (Model G) and French (INRA; Model I) system. Model evaluation was conducted using eight nitrogen balance studies performed in El Salvador, Kenya and Peru (n = 392 individual observations including lactating cows, heifers and steers). Concordance correlation coefficient, root mean square errors (RMSE), and mean biases were estimated to evaluate the models' adequacy in predicting nitrogen excretion. Input variables causing greatest variation in nitrogen excretion prediction were identified by a sensitivity analysis and adjusted. Model G was able to adequately (i.e., RMSE of <25% of observed mean, systematic error of <5% of the mean square error) predict TN excretion through a compensation between overestimation of UN excretion and underestimation of FN excretion. None of the models were able to adequately predict UN, FN, and different FN fractions. Model I adequately predicted FN (RMSE = 18%) when duodenal microbial crude protein flow was increased, and the intercept used to predict FN excretion was reduced from 4.30 to 3.82 g of nitrogen per kilogram of dry matter intake. These adjustments, however, were not sufficient to predict adequately UN excretion (RMSE = 38%), individual FN fractions (RMSE > 56%), and TN (RMSE = 22%) excretion, by Model I.

Untargeted Metabolomics Profiling for Determination of the Time since Deposition of Biofluids in a Forensic Context: A Proof-of-Concept for Urine, Saliva, and Semen in Addition to Blood.

In a criminal trial, the reconstruction of a crime is one of the fundamental steps of the prosecution process. Common questions, such as what happened, where and how it happened, and who made it happen, need to be solved. Biological evidence at crime scenes can be crucial in the determination of these fundamental questions. One of the more challenging riddles to solve is the when? A trace left at a crime scene can prove a person's presence at the crime scene. Knowledge about when it was deposited there, the time since deposition (TsD), would allow linking the person in space and time to the site. This could fortify allegations against a suspect or discharge accusations if proven to be outside of the temporal boundaries where a suspected crime had occurred. Determining the TsD has yet to become routine forensic casework, despite recent research efforts, especially for blood traces. However, next to blood, other biological traces are also commonly encountered in crime scenes. We here present a study to profile the metabolomes of artificially aged dried body fluid spots of blood, semen, saliva, and urine over 4 weeks by liquid chromatography high-resolution mass spectrometry and data-dependent acquisition. All four body fluids (BFs) exhibited diverse time-dependent changes, and a large number of molecular features (MF) were associated with TsD. Still, significant differences between the BFs were observed, limiting universal interpretability independent of the BF and facilitating a need to further study time-dependent changes of different BFs individually toward the goal of TsD estimation.

Quantitative Label-Free SERS Detection of Trace Fentanyl in Biofluids with a Freestanding Hydrophobic Plasmonic Paper Biosensor.

The prevalence of fentanyl abuse raises global public health concerns with an unprecedented surge in overdose deaths. Rapid identification and quantification of fentanyl in biofluids is of paramount importance to combat fentanyl abuse for law enforcement agencies and promptly treat patients for medical professionals. Herein, a freestanding surface-enhanced Raman spectroscopy (SERS) biosensor with excellent condensing enrichment capability, termed FrEnSERS biosensor, is reported for quantitative label-free detection of trace fentanyl in biofluids. This biosensor comprises a reduced graphene oxide membrane decorated with high-density hydrophobic Au nanostars. A combination of the high SERS enhancement and the focusing effect for analyte enrichment of the hydrophobic surface accounts for the remarkable SERS performance of the FrEnSERS biosensor. We demonstrate that the FrEnSERS biosensor achieves the sensitive and quantitative detection of fentanyl in both serum and urine over a wide dynamic range spanning more than 4 orders of magnitude, with a limit of detection of 0.47 ng/mL for serum samples and 0.73 ng/mL for urine samples. Our biosensor is sensitive, cost-effective, and reliable for rapid quantitative analysis of fentanyl in biofluids with great promise for forensic analysis and clinical diagnosis.