The developmental basis of fingerprint pattern formation and variation.

Fingerprints are complex and individually unique patterns in the skin. Established prenatally, the molecular and cellular mechanisms that guide fingerprint ridge formation and their intricate arrangements are unknown. Here we show that fingerprint ridges are epithelial structures that undergo a truncated hair follicle developmental program and fail to recruit a mesenchymal condensate. Their spatial pattern is established by a Turing reaction-diffusion system, based on signaling between EDAR, WNT, and antagonistic BMP pathways. These signals resolve epithelial growth into bands of focalized proliferation under a precociously differentiated suprabasal layer. Ridge formation occurs as a set of waves spreading from variable initiation sites defined by the local signaling environments and anatomical intricacies of the digit, with the propagation and meeting of these waves determining the type of pattern that forms. Relying on a dynamic patterning system triggered at spatially distinct sites generates the characteristic types and unending variation of human fingerprint patterns.

Limb development genes underlie variation in human fingerprint patterns.

Fingerprints are of long-standing practical and cultural interest, but little is known about the mechanisms that underlie their variation. Using genome-wide scans in Han Chinese cohorts, we identified 18 loci associated with fingerprint type across the digits, including a genetic basis for the long-recognized "pattern-block" correlations among the middle three digits. In particular, we identified a variant near EVI1 that alters regulatory activity and established a role for EVI1 in dermatoglyph patterning in mice. Dynamic EVI1 expression during human development supports its role in shaping the limbs and digits, rather than influencing skin patterning directly. Trans-ethnic meta-analysis identified 43 fingerprint-associated loci, with nearby genes being strongly enriched for general limb development pathways. We also found that fingerprint patterns were genetically correlated with hand proportions. Taken together, these findings support the key role of limb development genes in influencing the outcome of fingerprint patterning.

The long-term genetic stability and individual specificity of the human gut microbiome.

By following up the gut microbiome, 51 human phenotypes and plasma levels of 1,183 metabolites in 338 individuals after 4 years, we characterize microbial stability and variation in relation to host physiology. Using these individual-specific and temporally stable microbial profiles, including bacterial SNPs and structural variations, we develop a microbial fingerprinting method that shows up to 85% accuracy in classifying metagenomic samples taken 4 years apart. Application of our fingerprinting method to the independent HMP cohort results in 95% accuracy for samples taken 1 year apart. We further observe temporal changes in the abundance of multiple bacterial species, metabolic pathways, and structural variation, as well as strain replacement. We report 190 longitudinal microbial associations with host phenotypes and 519 associations with plasma metabolites. These associations are enriched for cardiometabolic traits, vitamin B, and uremic toxins. Finally, mediation analysis suggests that the gut microbiome may influence cardiometabolic health through its metabolites.

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

A radiomics-based brain network in T1 images: construction, attributes, and applications.

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Membranes are functionalized by a proteolipid code.

Membranes are protein and lipid structures that surround cells and other biological compartments. We present a conceptual model wherein all membranes are organized into structural and functional zones. The assembly of zones such as receptor clusters, protein-coated pits, lamellipodia, cell junctions, and membrane fusion sites is explained to occur through a protein-lipid code. This challenges the theory that lipids sort proteins after forming stable membrane subregions independently of proteins.

Single cell sequencing delineates T-cell clonality and pathogenesis of the parapsoriasis disease group.

BACKGROUND: Mycosis fungoides (MF), the most common cutaneous T-cell lymphoma, is often underdiagnosed in early stages due to similarities with benign dermatoses such as atopic dermatitis (AD). Furthermore, the delineation from so-called "parapsoriasis en plaque," a disease that can appear either in a small- or large-plaque form, is still controversial. OBJECTIVE: To characterize the parapsoriasis disease spectrum. METHODS: We performed single-cell RNA sequencing of skin biopsies from patients within the parapsoriasis-to-early-stage MF spectrum, stratified for small and large plaques, and compared them to AD, psoriasis and healthy control skin. RESULTS: 6 out of 8 large-plaque lesions harbored either an expanded alpha/beta or gamma/delta T-cell clone with downregulation of CD7 expression, consistent with a diagnosis of early-stage MF. By contrast, 6 out of 7 small-plaque lesions were polyclonal in nature thereby lacking a lymphomatous phenotype, and also revealed a less inflammatory microenvironment than early-stage MF or AD. Of note, polyclonal small- and large-plaque lesions characteristically harbored a population of NPY+ innate lymphoid cells and displayed a stromal signature of complement upregulation and antimicrobial hyperresponsiveness in fibroblasts and sweat gland cells, respectively. These conditions were clearly distinct from AD or psoriasis, which uniquely harbored CD3+CRTH2+ IL13-expressing "Th2A" cells or strong type 17 inflammation, respectively. CONCLUSION: These data position polyclonal small- and large-plaque dermatitis lesions as a separate disease entity, that characteristically harbors a so far undescribed ILC population. We thus propose the new term "polyclonal parapsoriasis en plaque" to this kind of lesions, as they can be clearly differentiated from early and advanced-stage MF, psoriasis and AD on several cellular and molecular levels.

Single-cell RNA sequencing of mid-to-late stage spider embryos: new insights into spider development.

BACKGROUND: The common house spider Parasteatoda tepidariorum represents an emerging new model organism of arthropod evolutionary and developmental (EvoDevo) studies. Recent technical advances have resulted in the first single-cell sequencing (SCS) data on this species allowing deeper insights to be gained into its early development, but mid-to-late stage embryos were not included in these pioneering studies. RESULTS: Therefore, we performed SCS on mid-to-late stage embryos of Parasteatoda and characterized resulting cell clusters by means of in-silico analysis (comparison of key markers of each cluster with previously published information on these genes). In-silico prediction of the nature of each cluster was then tested/verified by means of additional in-situ hybridization experiments with additional markers of each cluster. CONCLUSIONS: Our data show that SCS data reliably group cells with similar genetic fingerprints into more or less distinct clusters, and thus allows identification of developing cell types on a broader level, such as the distinction of ectodermal, mesodermal and endodermal cell lineages, as well as the identification of distinct developing tissues such as subtypes of nervous tissue cells, the developing heart, or the ventral sulcus (VS). In comparison with recent other SCS studies on the same species, our data represent later developmental stages, and thus provide insights into different stages of developing cell types and tissues such as differentiating neurons and the VS that are only present at these later stages.

Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability.

The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.

Genetic diversity analysis and DNA fingerprint construction of Zanthoxylum species based on SSR and iPBS markers.

Zanthoxylum is a versatile economic tree species utilized for its spice, seasoning, oil, medicinal, and industrial raw material applications, and it has a lengthy history of cultivation and domestication in China. This has led to the development of numerous cultivars. However, the phenomenon of mixed cultivars and confusing names has significantly obstructed the effective utilization of Zanthoxylum resources and industrial development. Consequently, conducting genetic diversity studies and cultivar identification on Zanthoxylum are crucial. This research analyzed the genetic traits of 80 Zanthoxylum cultivars using simple sequence repeat (SSR) and inter-Primer Binding Site (iPBS) molecular markers, leading to the creation of a DNA fingerprint. This study identified 206 and 127 alleles with 32 SSR markers and 10 iPBS markers, respectively, yielding an average of 6.4 and 12.7 alleles (Na) per marker. The average polymorphism information content (PIC) for the SSR and iPBS markers was 0.710 and 0.281, respectively. The genetic similarity coefficients for the 80 Zanthoxylum accessions ranged from 0.0947 to 0.9868 and from 0.2206 to 1.0000, with mean values of 0.3864 and 0.5215, respectively, indicating substantial genetic diversity. Cluster analysis, corroborated by principal coordinate analysis (PCoA), categorized these accessions into three primary groups. Analysis of the genetic differentiation among the three Zanthoxylum (Z. bungeanum, Z. armatum, and Z. piperitum) populations using SSR markers revealed a mean genetic differentiation coefficient (Fst) of 0.335 and a gene flow (Nm) of 0.629, suggesting significant genetic divergence among the populations. Molecular variance analysis (AMOVA) indicated that 65% of the genetic variation occurred within individuals, while 35% occurred among populations. Bayesian model-based analysis of population genetic structure divided all materials into two groups. The combined PI and PIsibs value of the 32 SSR markers were 4.265 × 10- 27 and 1.282 × 10- 11, respectively, showing strong fingerprinting power. DNA fingerprints of the 80 cultivars were established using eight pairs of SSR primers, each assigned a unique numerical code. In summary, while both markers were effective at assessing the genetic diversity and relationships of Zanthoxylum species, SSR markers demonstrated superior polymorphism and cultivar discrimination compared to iPBS markers. These findings offer a scientific foundation for the conservation and sustainable use of Zanthoxylum species.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

Representation of molecules for drug response prediction.

The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.

Mass spectrometry in food authentication and origin traceability.

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Evaluating fingerprint-like patterns in the healthy Henle fiber layer using enface OCT imaging.

PURPOSE: Enface OCT may disclose a distinct "fingerprint-like' pattern within the HFL in various macular disorders. This study aims to investigate the frequency and characteristics of this pattern in healthy eyes and identify potential factors influencing its visibility. METHODS: Two, independent masked reading center graders evaluated for the presence and prominence of a fingerprint pattern in the Henle fiber layer (HFL) on enface OCT images from 33 healthy subjects (66 eyes). The prominence of the pattern was rated qualitatively using a 0-3 scale, with 3 indicating the strongest prominence. Tilt angles (relative to the normal/perpendicular at the center) of the retina were measured on horizontal and vertical B-scans, and the retinal curvature was assessed using ImageJ, in order to determine the impact of the incident light angle on the visibility and prominence of the fingerprint pattern. Inter-grader agreement using Cohen's kappa and the frequency and percentage of patterns in the entire enface image and in each quadrant were calculated and compared using the Friedman test with Dunn's post-test. A generalized estimating equation (GEE) was used to analyze the association between these metrics and fingerprint prominence. RESULTS: Substantial inter-grader agreement was observed (Cohen's kappa = 0.71) for assessing the prominence of the fingerprint pattern. Over 70% of eyes exhibited some evidence of the pattern (score ≥1). Significant difference in pattern prominence across quadrants was detected (p < 0.05), with lowest prominence in the temporal quadrant (p < 0.001 for pairwise comparisons against all other quadrants). The GEE analysis to account for the extent of the effect of scan tilt angle and RPE curvature was not able to predict the prominence of the fingerprint pattern, highlighting that angle of incidence (of the scanning laser light) alone could not explain the pattern. CONCLUSIONS: This study confirms that a fingerprint-like pattern within the HFL can also be observed in healthy eyes, challenging the notion that this finding is only manifest in the setting of disease. In addition, the lack of correlation with angle of incident light suggests that the pattern may be related to other intrinsic characteristics of the HFL.

Finite element analysis of Neanderthal and early Homo sapiens maxillary central incisor.

Neanderthal anterior teeth are very large and have a distinctive morphology characterized by robust 'shovel-shaped' crowns. These features are frequently seen as adaptive responses in dissipating heavy mechanical loads resulting from masticatory and non-masticatory activities. Although the long-standing debate surrounding this hypothesis has played a central role in paleoanthropology, is still unclear if Neanderthal anterior teeth can resist high mechanical loads or not. A novel way to answer this question is to use a multidisciplinary approach that considers together tooth architecture, dental wear and jaw movements. The aim of this study is to functionally reposition the teeth of Le Moustier 1 (a Neanderthal adolescent) and Qafzeh 9 (an early Homo sapiens adolescent) derived from wear facet mapping, occlusal fingerprint analysis and physical dental restoration methods. The restored dental arches are then used to perform finite element analysis on the left central maxillary incisor during edge-to-edge occlusion. The results show stress distribution differences between Le Moustier 1 and Qafzeh 9, with the former displaying higher tensile stress in enamel around the lingual fossa but lower concentration of stress in the lingual aspect of the root surface. These results seem to suggest that the presence of labial convexity, lingual tubercle and of a large root surface in Le Moustier 1 incisor helps in dissipating mechanical stress. The absence of these dental features in Qafzeh 9 is compensated by the presence of a thicker enamel, which helps in reducing the stress in the tooth crown.

Detection and elimination of trace d-lactic acid in lignocellulose biorefining chain: Generation, flow, and impact on chiral lactide synthesis.

High chiral purity of lactic acid is a crucial indicator for the synthesis of chiral lactide as the primary intermediate chemical for ring-open polymerization of high molecular weight polylactic acid (PLA). Lignocellulose biomass is the most promising carbohydrate feedstock for commercial production of PLA, but the presence of trace d-lactic acid in the biorefinery chain adversely affects the synthesis and quality of chiral lactide. This study analyzed the fingerprint of trace  d-lactic acid in the biorefinery chain and found that the major source of  d-lactic acid comes from lignocellulose feedstock. The naturally occurring lactic acid bacteria and water-soluble carbohydrates in lignocellulose feedstock provide the necessary conditions for  d-lactic acid generation. Three strategies were proposed to eliminate the generation pathway of  d-lactic acid, including reduction of moisture content, conversion of water-soluble carbohydrates to furan aldehydes in pretreatment, and conversion to  l-lactic acid by inoculating engineered  l-lactic acid bacteria. The natural reduction of lactic acid content in lignocellulose feedstock during storage was observed due to the lactate oxidase-catalyzed oxidation of  l- and  d-lactic acids. This study provided an important support for the production of cellulosic  l-lactic acid with high chiral purity.

Sexual dimorphism and topological variability in fingerprint ridge density in a north-west Indian population.

The science of fingerprints is very crucial in criminal investigation as it helps identify perpetrators or victims of a crime. Fingerprint ridge density (FPRD), which refers to the number of ridges within a specific area on the epidermal skin layer of the distal phalanges in humans, has been found to differ between males and females. This study attempts to estimate the sex from FPRD and evaluates the diversity in FPRD across several topological areas. The study involves 208 participants (120 males, 88 females) between the ages 18 to 25 years from a North-west Indian population. Fingerprints were collected, and FPRD was accessed in radial, ulnar, and proximal areas as recommended by Gutierrez-Redomero et al. (Forensic Sci Int 180(1):17-22, 2008). FPRD has been quantified using the techniques described by Acree (Forensic Sci Int 102(1):35-44, 1999). When evaluating FPRD in the lateral pocket loops and twin loops, the proximal-side core was considered. The study reveals that males have a mean fingerprint ridge density of 12.82 ridges/25 mm2 while females have 13.01 ridges/25 mm2. Females have higher fingerprint ridge density solely in the proximal area; males have higher fingerprint ridge density in both radial and ulnar areas. In conclusion, this research underscores the potential of fingerprint ridge density as a parameter for investigating population variations and individual identification. Future studies on fingerprint ridge density in India's diverse population will help establish reference ranges, allowing for sex and likely population group estimation, making it a valuable tool for preliminary examinations and exclusion criteria for sex estimation in crime scene investigations.

Fingerprinting Organofluorine Molecules via Position-Specific Isotope Analysis.

Organofluorine substances are found in a wide range of materials and solvents commonly used in industry and homes, as well as pharmaceuticals and pesticides. In the environment, organofluorine molecules are now recognized as an important class of anthropogenic pollutants. Fingerprinting organofluorine compounds via their carbon isotope ratios (13C/12C) is crucial for correlating molecules with their source. Here we apply a 19F nuclear magnetic resonance spectroscopy (NMR) technique to obtain the first position-specific carbon isotope ratios for a diverse set of organofluorine molecules. In contrast to traditional isotope ratio mass spectrometry, the 19F NMR method provides 13C/12C isotope ratios at each carbon position where a C-F bond is present, and does not require fragmentation or combustion to CO2, overcoming challenges posed by the robust C-F covalent bonds. The method was validated with 2,2,2-trifluoroethanol, and applied to analyze heptafluorobutanoic acid, 5-fluorouracil and fipronil. Results reveal distinct intramolecular carbon isotope distributions, enabling differentiation of chemically identical molecules. Notably, the NMR method accurately analyzes carbon isotopes within target molecules despite impurities. Potential applications include the detection of counterfeit products and drugs, and ultimately pollution tracking in the environment.

Application of a robust MALDI mass spectrometry approach for bee pollen investigation.

Pollen collected by pollinators can be used as a marker of the foraging behavior as well as indicate the botanical species present in each environment. Pollen intake is essential for pollinators' health and survival. During the foraging activity, some pollinators, such as honeybees, manipulate the collected pollen mixing it with salivary secretions and nectar (corbicular pollen) changing the pollen chemical profile. Different tools have been developed for the identification of the botanical origin of pollen, based on microscopy, spectrometry, or molecular markers. However, up to date, corbicular pollen has never been investigated. In our work, corbicular pollen from 5 regions with different climate conditions was collected during spring. Pollens were identified with microscopy-based techniques, and then analyzed in MALDI-MS. Four different chemical extraction solutions and two physical disruption methods were tested to achieve a MALDI-MS effective protocol. The best performance was obtained using a sonication disruption method after extraction with acetic acid or trifluoroacetic acid. Therefore, we propose a new rapid and reliable methodology for the identification of the botanical origin of the corbicular pollens using MALDI-MS. This new approach opens to a wide range of environmental studies spanning from plant biodiversity to ecosystem trophic interactions.

Potential of ion mobility mass spectrometry in cellulose ether analysis: substitution pattern of hydroxyethyl celluloses.

Ion mobility mass spectrometry (ESI-tims-ToF-MS, syringe pump infusion) has been applied to glucose and oligosaccharide ethers derived from hydroxyethyl-methyl celluloses (HEMC) and hydroxyethyl celluloses (HEC) after permethylation and partial depolymerization: by hydrolysis without or with subsequent reductive amination with m-amino benzoic acid (mABA) or by reductive cleavage. As model compounds without tandem substitution methoxyethylated methylcellulose was used. Regioisomeric glucose ethers were separated according to their ion mobility, and positions of substitution could be assigned. Glucose ethers including isomers with tandem substitution showed additional signals with a smaller collision cross-section (CCS) than core-substituted isomers. Positional isomers of cellobiose ethers were only partly resolved due to too high complexity but showed a characteristic fingerprint that might allow classifying samples. Relative intensities of signals of glucose ether isomers could only be quantified in case of ABA derivatives with its fixed charge, while sodium adducts of methoxyethyl ethers showed an influence of the MeOEt position on ion yield. Results were in very good agreement with reference analysis. [M + Na]+ adducts of α- and ß-anomers of glucose derivatives were separated in IM, complicating position assignment. This could be overcome by reductive cleavage of the permethylated HE(M)C yielding 1,5-anhydroglucitol-terminated oligosaccharides, showing the best resolved fingerprints of the cellobiose ethers of a particular cellulose ether. With this first application of ion mobility MS to the analysis of complex cellulose ethers, the promising potential of this additional separation dimension in mass spectrometry is demonstrated and discussed.