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Results for “cell deconvolution”

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PubMed2025

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.

Humans↗

PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗

PubMed2025

Continuous DNA Methylation Deconvolution-Based Surrogate for B-Cell Differentiation State in CLL.

Chronic Lymphocytic Leukemia (CLL) is clinically divided into IGHV mutated (M-CLL) and IGHV unmutated (U-CLL) subtypes, which are thought to arise from distinct cells of origin along the B-cell differentiation pathway. We measured genome-scale DNA methylation in purified CLL samples ( n = 89) and utilized reference-based cell deconvolution techniques to develop a continuous metric of epigenetic similarity across a B-naive-like to B-memory-like scale (B-Index). B-Index accurately classifies CLL into clinical subtypes (98.8%), has a stronger epigenetic signal than IGHV gene percent identity, and demonstrates additional epigenetic signal within the M-CLL subgroup. We demonstrate that U-CLL is epigenetically more similar to B-memory than B-naive cells and reconcile previous reports of a B-naive-like epigenetic signal. The B-memory-like program of U-CLL is enriched for binding sites of transcription factors related to the germinal center activation pathway. Our findings provide epigenetic evidence for discerning CLL mechanisms of initiation and cell of origin. We also identified an epigenetic signal associated with tumor burden, which may have some relation to viral infections such as Epstein-Barr-Virus. Our cell-type deconvolution-based approach to developing a continuous metric for CLL epigenetic differentiation state can be applied to other tumors with multiple subtypes across differentiation stages.

B-memory-like↗

PubMed2025

Sparse deconvolution of cell type medleys in spatial transcriptomics.

Mapping cell distributions across spatial locations with whole-genome coverage is essential for understanding cellular responses and signaling However, current deconvolution models aim to estimate the proportions of distinct cell types in each spatial transcriptomics spot by integrating reference single-cell data. These models often assume strong overlap between the reference and spatial datasets, neglecting biology-grounded constraints such as sparsity and cell-type variations, as well as technical sparsity. As a result, these methods rely on over-permissive algorithms that ignore given constraints leading to inaccurate predictions, particularly in heterogeneous or unmatched datasets. We introduce Weight-Induced Sparse Regression (WISpR), a machine learning algorithm that integrates spot-specific hyperparameters and sparsity-driven modeling. Unlike conventional approaches that neglect biology-grounded constraints, WISpR accurately predicts cell-type distributions while preserving biological coherence, i.e., spatially and functionally consistent cell-type localization, even in unmatched datasets. Benchmarking against five alternative methods across ten datasets, WISpR consistently outperformed competitors and predicted cellular landscapes in both normal and cancerous tissues. By leveraging sparse cell-type arrangements, WISpR provides biologically informed, high-resolution cellular maps. Its ability to decode tissue organization in both healthy and diseased states highlights WISpR's practical utility for spatial transcriptomics, particularly in challenging settings involving noise, sparsity, or reference mismatches.

Humans↗

PubMed2026

The establishment of prostate-specific, SKP2 humanized mice by CRISPR knock-in method reveals neoplastic initiation and microenvironmental reprogramming.

Genetic inactivation of SKP2 has been shown to effectively prevent cancer initiation and block tumorigenesis. However, direct in vivo evidence for SKP2 on cancer initiation and prostatic microenvironment is still lacking and a SKP2 humanized mouse model is critical for developing prostate cancer immunoprevention approaches through targeting SKP2. We therefore have established a prostate-specific human SKP2 knock-in mouse model driven by an endogenous mouse probasin promoter. Overexpression of hSKP2 induces PIN and low-grade carcinoma. RNA-sequencing analysis revealed significant gene expression alterations in EMT, extracellular matrix, and interferon signaling. Single-cell deconvolution showed an increase of fibroblast population and a decrease of CD8+ T cell and B cell populations. Consistent with these results from the SKP2 humanized mouse, SKP2 protein is overexpressed in human prostatic hyperplasia, PIN and prostate adenocarcinoma compared to normal prostate tissues. Overexpression of SKP2 markedly increased cell migration and invasion and induced the gene expression of EMT and interferon pathways. Inhibition of SKP2 signaling by Flavokawain A and C1 reverses EMT and affects EMT and interferon-related gene expression. In addition, paired prostate organoids were derived from SKP2 humanized and wild-type mice for drug screening and validated by known SKP2 inhibitors, Flavokawain A and C1. Both of which selectively decreased viability and altered the morphologies of organoids of hSKP2 knock-in rather than wild-type mice. Our studies provide a well-characterized prostate-specific hSKP2 knock-in mouse model and offer new mechanistic insights for understanding the oncogenic role of SKP2 in shaping the prostatic microenvironment during early carcinogenesis.

Animals↗

PubMed2026

Transcriptomic profiling across stages of non-muscle-invasive bladder cancer identifies fibroblast activation protein-alpha as a stromal biomarker associated with progression.

BACKGROUND: T1 non-muscle-invasive bladder cancer (NMIBC) represents a biologically aggressive subgroup with substantial heterogeneity in recurrence and progression risk. Current clinicopathological risk stratification tools lack sufficient precision to identify patients at the highest risk of progression to muscle-invasive bladder cancer (MIBC). OBJECTIVE: To characterize transcriptomic differences between T1 and < T1 (Ta/Tis) NMIBC and to explore the association of fibroblast activation protein-α (FAP) gene expression with disease progression. METHODS: Transcriptomic profiling was performed on formalin-fixed paraffin-embedded (FFPE) tumor tissue from 66 patients with primary, treatment-naïve NMIBC and 5 patients with T2 disease (included for exploratory comparisons). Analyses included differential gene expression, gene set enrichment analysis (GSEA), molecular subtyping, immune cell deconvolution, and evaluation of FAP expression in relation to recurrence and progression. External validation of FAP was conducted in three independent NMIBC cohorts. RESULTS: T1 tumors demonstrated a distinct transcriptomic profile compared with < T1 tumors, characterized by enrichment of cell cycle-related and metabolic pathways and a higher prevalence of aggressive molecular subtypes. Despite these molecular differences, no statistically significant differences in recurrence-free, progression-free, cancer-specific, and overall survival were observed, likely reflecting limited event numbers. Among recurrent tumors, early recurrences (≤ 24 months) were associated with epithelial-mesenchymal transition signatures. FAP expression increased with tumor stage (p = 0.0005) and was associated with progression (p = 0.002) and mortality (p = 0.01). Patients with tumors in the highest quartile of FAP expression had worse progression-free survival. This association was consistently observed in three external NMIBC cohorts. CONCLUSIONS: T1 NMIBC exhibits distinct transcriptomic features suggestive of increased biological aggressiveness. Elevated FAP expression is reproducibly associated with progression risk across multiple cohorts, supporting its potential role as a biomarker of aggressive disease. Given the limited number of progression events, these findings should be considered hypothesis-generating and warrant prospective validation before clinical implementation.

Humans↗

PubMed2026

Dynamic Alterations in the Blood Transcriptome Characterize Drug Use Behavior and Co-Morbidities in Cocaine Use Disorder: A Preliminary Study.

Individuals with cocaine use disorder (CUD) who attempt abstinence experience craving and relapse that can benefit from multimodal treatment monitoring. Longitudinal studies linking behavioral manifestations in CUD to the blood transcriptome are not only limited but also computationally complex. Therefore, we developed an analytical pipeline to investigate the connection between drug use behaviors during abstinence and change in the blood transcriptome. We conducted a longitudinal study with CUD (n = 12 subjects) and collected behavioral metrics and blood RNA-seq at baseline, 3, 6, and 9 months. Our analytical pipeline of the high-dimensional data encompasses hierarchical k-means clustering to classify subjects to responder groups based on behavioral scores and abstinence duration, in silico cell deconvolution, differential analysis with correlated multivariate testing over time, gene set enrichment analysis, and gene co-expression with time splines and RNA-seq data. The pipeline captured dynamic changes in behavioral scores and abstinence duration in responder groups. Genes showing differential transcript-level expression were enriched in substance use and cardiovascular disease-associated genetic risk loci in responder groups. Lastly, time-dependent gene co-expression revealed dynamic changes related to immune processes, cell cycle, RNA-protein synthesis, and second messenger signaling for days of abstinence. This is a preliminary investigation, providing an innovative and scalable pipeline for blood-based longitudinal RNA-seq studies in CUD, potentially applicable to other substance use disorders. It outlines a data-driven approach for analyzing composite longitudinal drug use behavioral phenotypes with blood-based transcriptomics. We also demonstrate changes in drug use behaviors and the blood transcriptome during drug abstinence.

Humans↗

PubMed2024

Pterostilbene Targets Hallmarks of Aging in the Gene Expression Landscape in Blood of Healthy Rats.

SCOPE: Polyphenols from the phytoestrogen group, including pterostilbene (PTS), are known for their antioxidant, anti-inflammatory, and anti-cancer effects. In recent reports, phytoestrogens attenuate age-related diseases; however, their pro-longevity effects in healthy models in mammals remain unknown. As longevity research demonstrates age-related transcriptomic signatures in human blood, the current study hypothesizes that phytoestrogen-supplemented diet may induce changes in gene expression that ultimately confer pro-longevity benefits. METHODS AND RESULTS: In the present study, RNA sequencing is conducted to determine transcriptome-wide changes in gene expression in whole blood of healthy rats consuming diets supplemented with phytoestrogens. Ortholog cell deconvolution is applied to analyze the omics data. The study discovered that PTS leads to changes in the gene expression landscape and PTS-target genes are associated with functions counteracting hallmarks of aging, including genomic instability, epigenetic alterations, compromised autophagy, mitochondrial dysfunction, deregulated nutrient sensing, altered intercellular interaction, and loss of proteostasis. These functions bridge together under anti-inflammatory effects through multiple pathways, including immunometabolism, where changes in cellular metabolism (e.g., ribosome biogenesis) impact the immune system. CONCLUSION: The findings provide a rationale for pre-clinical and clinical longevity studies and encourage investigations on PTS in maintaining cellular homeostasis, decelerating the process of aging, and improving conditions with chronic inflammation.

Animals↗

PubMed2025

Detection of cell-type-specific differentially methylated regions in epigenome-wide association studies.

MOTIVATION: DNA methylation at cytosine-phosphate-guanine (CpG) sites is one of the most important epigenetic markers. Therefore, epidemiologists are interested in investigating DNA methylation in large cohorts through epigenome-wide association studies (EWAS). However, the observed EWAS data are bulk data with signals aggregated from distinct cell types. Deconvolution of cell-type-specific signals from EWAS data is challenging because phenotypes can affect both cell-type proportions and cell-type-specific methylation levels. Recently, there has been active research on detecting cell-type-specific risk CpG sites for EWAS data. However, existing methods all assume that the methylation levels of different CpG sites are independent and perform association detection for each CpG site separately. Although these methods significantly improve the detection at the aggregated-level-identifying a CpG site as a risk CpG site as long as it is associated with the phenotype in any cell type, they have low power in detecting cell-type-specific associations for EWAS with typical sample sizes. RESULTS: Here, we develop a new method, Fine-scale inference for Differentially Methylated Regions (FineDMR), to borrow strengths of nearby CpG sites to improve the cell-type-specific association detection. Via a Bayesian hierarchical model built upon Gaussian process functional regression, FineDMR takes advantage of the spatial dependencies between CpG sites. FineDMR can provide cell-type-specific association detection as well as output subject-specific and cell-type-specific methylation profiles for each subject. Simulation studies and real data analysis show that FineDMR substantially improves the power in detecting cell-type-specific associations for EWAS data. AVAILABILITY AND IMPLEMENTATION: FineDMR is freely available at https://github.com/JiaRuofan/Detection-of-Cell-type-specific-DMRs-in-EWAS.

DNA Methylation↗

PubMed2025

Immune Cell Type-Specific DNA Methylation Regions Associate With 24-Hour Blood Pressure Regulation in Black People.

BACKGROUND: DNA methylation and immune cells have been linked to blood pressure (BP) regulation and the development of hypertension. However, the immune cell profiles and the cell type-specific DNA methylation associated with BPs remain unclear. METHODS: This study evaluates the 19 cell type deconvolution algorithms using reduced representation bisulfite sequencing data, comparing them to in silico mixtures derived from whole-genome bisulfite sequencing. The top-performing algorithm, Epigenetic Dissection of Intra-Sample Heterogeneity (EpiDISH)-Robust Partial Correlations, was applied to 281 Black inpatients with 24-hour BP monitoring. The immune cell profiles and cell type-specific DNA methylation regions associated with these BP phenotypes were further investigated using regression analysis. RESULTS: In patients with hypertension, B-cell and CD4 effector memory T-cell abundances were significantly elevated. Monocyte and CD8 effector memory T-cell fractions positively correlated with nighttime BP, and CD3 T cells were inversely associated with office BP. These associations remained robust after covariate adjustments and were partially validated in the Medical Information Mart for Intensive Care-IV cohort. For the first time, we identified several cell type-specific DNA methylation regions as being associated with BP phenotypes and patterns across 13 immune cells, with approximately one third predominantly found in effector CD8 T cells. CONCLUSIONS: These findings provide novel insights into the epigenetically regulated immune mechanisms underlying BP regulation and identify potential targets for hypertension management.

Humans↗

PubMed2026

Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics.

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data are essential. Unfortunately, accurately resolving cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires - inherently imperfect - cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3272 proteins, using cross-validation to determine regularisation parameters, and identified 539 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

Journal Article↗

PubMed2026

Endothelial cell-specific DNA methylation alterations in breast cancer.

DNA methylation alterations are well-established contributors to carcinogenesis, yet, in the tumor microenvironment (TME), patterns of lineage and cell-specific methylation alterations are not well understood. Single-cell DNA methylation profiling in the TME is limited by technical challenges and high costs. Here, we use bulk DNA methylation, cell type deconvolution (HiTIMED), and an interaction testing framework (CellDMC) to identify reproducible, computationally inferred lineage-specific epigenetic alterations in the TME supported by orthogonal data sources. Tumor endothelial cells (TECs), critical regulators of angiogenesis, vascular permeability, and immune cell trafficking, acquire structural and functional abnormalities that promote tumor growth. We hypothesize that TECs have altered DNA methylation compared with endothelial cells in non-tumor tissues. In genome-scale methylation data from discovery and validation datasets (tumor n = 1071; non-tumor n = 415), we identify and validate >4500 TEC-specific CpGs with altered methylation, many mapping to genes involved in angiogenesis and endothelial function. Integration with gene expression data indicates that TEC-specific methylation alterations may reprogram transcriptional networks controlling angiogenesis. High-resolution, cell lineage-specific epigenetic landscapes can be inferred from bulk methylation data, implicating TEC-specific DNA methylation alterations as potential drivers of cancer angiogenesis and vascular dysfunction and providing a framework for future mechanistic and translational studies of the tumor vasculature.

DNA Methylation↗

PubMed2025

Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods.

Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available.

Humans↗

PubMed1992

Evidence that androgen negative feedback regulates hypothalamic gonadotropin-releasing hormone impulse strength and the burst-like secretion of biologically active luteinizing hormone in men.

Testosterone injections or pharmacological amounts of dihydrotestosterone infused in men, and androgen-secreting tumors in women, can suppress plasma LH bioactivity assessed in an in vitro rat Leydig-cell bioassay. However, such observations do not define the physiological nature of endogenous androgen feedback actions on the hypothalamo-pituitary axis. To explore the feedback role of endogenous androgen on the male gonadotropic axis, we used a potent, selective, nonsteroidal competitive antagonist of the androgen receptor, flutamide HCl. Eight young men (ages 21-30) each received flutamide (750 mg orally daily x 3 days) and placebo and underwent blood sampling at 10-min intervals for 28 h, the last 2 h of which included two consecutive iv pulses of GnRH (10 micrograms). Plasma bioactive LH concentrations were measured in the rat Leydig cell bioassay. Deconvolution analysis was used to evaluate the number, amplitude, mass, and duration of bioactive LH secretory bursts and simultaneously estimate the half-life of endogenous LH. Flutamide treatment increased mean plasma bioactive LH concentrations from 27 +/- 2.3 to 54 +/- 9.1 IU/L (P = 0.018). Increased LH concentrations were achieved by a significantly amplified mass of LH secreted per burst, which rose from 14 +/- 1.8 (control) to 24 +/- 2.8 (flutamide) IU/L distribution volume. The amplitude (maximal secretion rate) of bioactive LH release episodes also increased from 1.3 +/- 0.21 (control) to 2.2 +/- 0.29 (flutamide) IU/L/min. These responses were specific, since flutamide did not influence bioactive LH half-life [49 +/- 6.5 (control) vs 52 +/- 4.1 (flutamide) min], LH secretory burst duration, frequency, or interburst interval. The total 24-h production rate of bioactive LH rose significantly from 310 +/- 35 (control) to 570 +/- 82 (flutamide) IU/L.day. In contrast, no features of LH secretory bursts evoked by exogenous GnRH pulses were altered significantly by antiandrogen. In summary, in vivo blockade of endogenous androgen negative feedback actions in normal men selectively amplifies the mass and amplitude of bioactive LH secretory bursts without altering their number or duration, or the half-life of LH, or the amount of LH released in response to exogenous GnRH. Therefore, we infer that in the steroid milieu of normal men endogenous androgen acting via the androgen receptor can negatively regulate hypothalamic GnRH stimulus strength, and hence the rate and mass of biologically active LH secretion in vivo.

Activity Cycles↗

PubMed2023

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.

BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data. METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best. RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis. CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

Humans↗

PubMed1975

An analysis of lamellar x-ray diffraction from disordered membrane multilayers with application to data from retinal rod outer segments.

Oriented multilayers containing a membrane pair within the unit cell potentially possess both lattice disorder and substitution disorder. Lattice disorder occurs when there is a lack of long-range order in the lattice spacings produced by a variation in the nearest neighbor distances between unit cells. A simple form of substitution disorder can arise from a variation in the separation of the two membranes within the unit cells in the multilayer. Lattice disorder produces a monotonically increasing width for higher order lamellar "reflections" while simple substitution disorder produces an incoherent intensity underlying the coherent intensity. A generalized Patterson function analysis has been developed for treating lamellar diffraction from lattice disordered multilayers. This analysis allows the identification of the autocorrelation function of the unit cell electron density profile and its subsequent deconvolution to provide the unit cell electron density profile. A recursive procedure has been developed for separating the incoherent intensity from the coherent intensity via a Gaussian probability model of the membrane intra-pair separation. In cases studied so far both disorders can be quantitatively accounted for and eliminated from interfering with the phasing of the coherent intensity or distorting the derived electron density profile. Lamellar X-ray diffraction data from intact retinal rods, using either film or position sensitive detectors, shows severe effects of both forms of disorder which have not been taken into account in past analysis of such data. We have applied our analysis to the data on dark adapted rod outer segments in electrophysiologically intact retinas of Chabre and Cavaggioni (unpublished). An electron density profile is derived at 25 A resolution. The lattice nearest neighbor spacing has a variation of +/- 19 A out of a 295 A repeat. The intra-unit cell membrane pair center to center distance of 88 A varies +/-8 A.

Animals↗

PubMed2026

Epigenetic and immunological alterations in umbilical cord blood of overweight/obese women with gestational diabetes mellitus: insights into DNA methylation signatures and immune cell dysregulation.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse maternal and neonatal outcomes. Epigenetic modifications may reflect intrauterine metabolic exposure and contribute to immune and metabolic alterations. This study aimed to explore DNA methylation profiles in umbilical cord blood from overweight and obese women with and without GDM. METHODS: Umbilical cord blood samples from 30 overweight/obese pregnant women (with and without GDM) were analyzed using the Illumina 850 K methylation array to identify differentially methylated positions (DMPs) and regions (DMRs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to assess the functional relevance of methylation changes. Immune cell composition was estimated using deconvolution analysis and further examined in an independent single-cell RNA sequencing (scRNA-seq) cohort. Lasso regression was applied to identify CpG sites associated with GDM status and construct a preliminary methylation-based classification model. RESULTS: A total of 23,331 hypermethylated and 29,501 hypomethylated DMPs were identified between women with and without GDM, with hypomethylation predominating. Enrichment analyses indicated associations with neurodevelopmental pathways, metabolic processes, immune regulation, and epigenetic modification. Immune deconvolution analysis suggested reduced proportions of CD4+ T cells (p < 0.05) and a trend toward decreased NK cells in the GDM group, alongside increased CD8+ T cells and neutrophils. Seven CpG sites were selected for model construction and demonstrated strong discriminatory performance within this cohort. CONCLUSION: This exploratory study identifies distinct cord blood DNA methylation patterns associated with GDM in overweight/obese pregnancies. The findings suggest potential links between epigenetic alterations and immune cell composition in GDM-exposed offspring. The identified CpG signature warrants further validation in larger, prospective cohorts to determine its clinical applicability.

Humans↗

PubMed1977

A direct analysis of lamellar x-ray diffraction from hydrated oriented multilayers of fully functional sarcoplasmic reticulum.

The profile structure of functional sarcoplasmic reticulum (SR) membranes was investigated by X-ray diffraction methods to a resolution of 10 A. The lamellar diffraction data from hydrated oriented multilayers of SR vesicles showed monotonically increasing widths for higher order lamellar reflections, indicative of simple lattice disorder within the multilayer. A generalized Patterson function analysis, previously developed for treating lamellar diffraction from lattice-disordered multilayers, was used to identify the autocorrelation function of the unit cell electron density profile. Subsequent deconvolution of this autocorrelation function provided the most probable unit cell electron density profile of the SR vesicle membrane pair. The resulting single membrane profile possesses marked asymmetry, suggesting that a major portion of the Ca++ -ATPase resides on the exterior of the vesicle. The electron density profile also suggests that the Ca++-dependent ATPase penetrates into the lipid hydrocarbon core of the SR membrane. Under conditions suitable for X-ray analysis, SR vesicles prepared as partially dehydrated oriented multilayers are shown to conserve most of their ATP-induced Ca++ uptake functionality, as monitored spectrophotometrically with the Ca++ indicator arsenazo III. This has been verified both in resuspensions of SR after centrifugation and slow partial dehydration, and directly in SR multilayers in a partially dehydrated state (20-30 percent water). Therefore, the profile structure of the SR membrane that we have determined may closely resemble that found in vivo.

Adenosine Triphosphatases↗