A spatially resolved single-cell genomic atlas of the adult human breast.

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.

Author Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Analysis of the Human Protein Atlas Image Classification competition.

Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.

Serum metabolite profiles are associated with the presence of advanced liver fibrosis in Chinese patients with chronic hepatitis B viral infection.

BACKGROUND: Accurate and noninvasive diagnosis and staging of liver fibrosis are essential for effective clinical management of chronic liver disease (CLD). We aimed to identify serum metabolite markers that reliably predict the stage of fibrosis in CLD patients. METHODS: We quantitatively profiled serum metabolites of participants in 2 independent cohorts. Based on the metabolomics data from cohort 1 (504 HBV associated liver fibrosis patients and 502 normal controls, NC), we selected a panel of 4 predictive metabolite markers. Consequently, we constructed 3 machine learning models with the 4 metabolite markers using random forest (RF), to differentiate CLD patients from normal controls (NC), to differentiate cirrhosis patients from fibrosis patients, and to differentiate advanced fibrosis from early fibrosis, respectively. RESULTS: The panel of 4 metabolite markers consisted of taurocholate, tyrosine, valine, and linoelaidic acid. The RF models of the metabolite panel demonstrated the strongest stratification ability in cohort 1 to diagnose CLD patients from NC (area under the receiver operating characteristic curve (AUROC) = 0.997 and the precision-recall curve (AUPR) = 0.994), to differentiate fibrosis from cirrhosis (0.941, 0.870), and to stage liver fibrosis (0.918, 0.892). The diagnostic accuracy of the models was further validated in an independent cohort 2 consisting of 300 CLD patients with chronic HBV infection and 90 NC. The AUCs of the models were consistently higher than APRI, FIB-4, and AST/ALT ratio, with both greater sensitivity and specificity. CONCLUSIONS: Our study showed that this 4-metabolite panel has potential usefulness in clinical assessments of CLD progression in patients with chronic hepatitis B virus infection.

Strategy for Intercorrelation Identification between Metabolome and Microbiome.

Accumulating evidence points to the strong and complicated associations between the metabolome and the microbiome, which play diverse roles in physiology and pathology. Various correlation analysis approaches were applied to identify microbe-metabolite associations. Given the strengths and weaknesses of the existing methods and considering the characteristics of different types of omics data, we designed a special strategy, called Generalized coRrelation analysis for Metabolome and Microbiome (GRaMM), for the intercorrelation discovery between the metabolome and microbiome. GRaMM can properly deal with two types of omics data, the effect of confounders, and both linear and nonlinear correlations by integrating several complementary methods such as the classical linear regression, the emerging maximum information coefficient (MIC), the metabolic confounding effect elimination (MCEE), and the centered log-ratio transformation (CLR). GRaMM contains four sequential computational steps: (1) metabolic and microbial data preprocessing, (2) linear/nonlinear type identification, (3) data correction and correlation detection, and (4) p value correction. The performances of GRaMM, including the accuracy, sensitivity, specificity, false positive rate, applicability, and effects of preprocessing and confounder adjustment steps, were evaluated and compared with three other methods in multiple simulated and real-world datasets. To our knowledge, GRaMM is the first strategy designed for the intercorrelation analysis between metabolites and microbes. The Matlab function and an R package were developed and are freely available for academic use (comply with GNU GPL.V3 license).

Evaluation of metabolite-microbe correlation detection methods.

Different correlation detection methods have been specifically designed for the microbiome data analysis considering the compositional data structure and different sequencing depths. Along with the speedy development of omics studies, there is an increasing interest in discovering the biological associations between microbes and host metabolites. This raises the need of finding proper statistical methods that facilitate the correlation analysis across different omics studies. Here, we comprehensively evaluated six different correlation methods, i.e., Pearson correlation, Spearman correlation, Sparse Correlations for Compositional data (SparCC), Correlation inference for Compositional data through Lasso (CCLasso), Mutual Information Coefficient (MIC), and Cosine similarity methods, for the correlations detection between microbes and metabolites. Three simulated and two real-world data sets (from public databases and our lab) were used to examine the performance of each method regarding its specificity, sensitivity, similarity, accuracy, and stability with different sparsity. Our results indicate that although each method has its own pros and cons in different scenarios, Spearman correlation and MIC outperform the others with their overall performances. A strategic guidance was also proposed for the correlation analysis between microbe and metabolite.

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies.

Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp.

Serum stearic acid/palmitic acid ratio as a potential predictor of diabetes remission after Roux-en-Y gastric bypass in obesity.

Endogenous fatty acid metabolism that results in elongation and desaturation lipid products is thought to play a role in the development of type 2 diabetes mellitus (T2DM). In this study, we evaluated the potential of estimated elongase and desaturase activities for use as predictive markers for T2DM remission after Roux-en-Y gastric bypass (RYGB). The results of a targeted metabolomics approach from 2 independent studies were used to calculate 24 serum FA concentration ratios (product/precursor). Gene expression data from an open public data set was also analyzed. In a longitudinal study of 38 obese diabetic patients with RYGB, we found higher baseline stearic acid/palmitic acid (S/P) ratio. This ratio reflects an elovl6-encoded elongase enzyme activity that has been found to be associated with greater possibility for diabetes remission after RYGB [odds ratio, 2.16 (95% CI 1.10-4.26)], after adjustment for age, gender, body mass index, diabetes duration, glycosylated hemoglobin A1c, and fasting C-peptide. Our results were validated by examination of postsurgical elovl6 gene expression in morbidly obese patients. The association of S/P with the metabolic status of obese individuals was further validated in a cross-sectional cohort of 381 participants. In summary, higher baseline S/P was associated with greater probability of diabetes remission after RYGB and may serve as a diagnostic marker in preoperative patient assessment. - Zhao, L., Ni, Y., Yu, H., Zhang, P., Zhao, A., Bao, Y., Liu, J., Chen, T., Xie, G., Panee, J., Chen, W., Rajani, C., Wei, R., Su, M., Jia, W., Jia, W. Serum stearic acid/palmitic acid ratio as a potential predictor of diabetes remission after Roux-en-Y gastric bypass in obesity.

Modification effect of fenofibrate therapy, a longitudinal epigenomic-wide methylation study of triglycerides levels in the GOLDN study.

BACKGROUND: Identification of interactions between epigenetic factors and treatments might lead to personalized intervention of diseases. This paper aims to examine the modification effect of fenofibrate therapy on the association of methylation levels and fasting blood triglycerides (TG), and the related biological pathways among methylation sites. RESULTS: Mixed-effects models were employed to assess pre- and posttreatment associations and drug modification effects simultaneously. Five cytosine-phosphate-guanine (CpG) sites were found to be associated with TG levels before and after the fenofibrate therapy: cg00574958, cg17058475, and cg01082498 on CPT1A gene, chromosome 11; cg03725309 on SARS, chromosome 1; and cg06500161 on ABCG1, chromosome 21. In addition, fenofibrate therapy modified the methylation levels on the following 4 CpG sites: cg20015535 (gene EGLN1, chromosome 1); cg24870738 (gene RNF220, chromosome 1); cg06891775 (gene LOC283050, chromosome 10); and cg00607630 (gene USP7, chromosome 16). Further, gene set enrichment analysis (GSEA) identified cancer- and metabolism-related pathways that were associated with TG-related CpG sites. CONCLUSIONS: We identified modification effects of fenofibrate on the associations between blood TG levels and several CpG sites. Pathway enrichment analysis indicated the alternations in some metabolism and cancer-related pathways. Our findings have important implications for future research in pharmacoepigenetics and personalized medicine.

High Throughput and Quantitative Measurement of Microbial Metabolome by Gas Chromatography/Mass Spectrometry Using Automated Alkyl Chloroformate Derivatization.

The ability to identify and quantify small molecule metabolites derived from gut microbial-mammalian cometabolism is essential for the understanding of the distinct metabolic functions of the microbiome. To date, analytical protocols that quantitatively measure a complete panel of microbial metabolites in biological samples have not been established but are urgently needed by the microbiome research community. Here, we report an automated high-throughput quantitative method using a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) platform to simultaneously measure over one hundred microbial metabolites in human serum, urine, feces, and Escherichia coli cell samples within 15 min per sample. A reference library was developed consisting of 145 methyl and ethyl chloroformate (MCF and ECF) derivatized compounds with their mass spectral and retention index information for metabolite identification. These compounds encompass different chemical classes including fatty acids, amino acids, carboxylic acids, hydroxylic acids, and phenolic acids as well as benzoyl and phenyl derivatives, indoles, etc., that are involved in a number of important metabolic pathways. Within an optimized range of concentrations and sample volumes, most derivatives of both reference standards and endogenous metabolites in biological samples exhibited satisfactory linearity (R2 > 0.99), good intrabatch reproducibility, and acceptable stability within 6 days (RSD < 20%). This method was further validated by examination of the analytical variability of 76 paired human serum, urine, and fecal samples as well as quality control samples. Our method involved using high-throughput sample preparation, measurement with automated derivatization, and rapid GC/TOFMS analysis. Both techniques are well suited for microbiome metabolomics studies.

Factors affecting separation and detection of bile acids by liquid chromatography coupled with mass spectrometry in negative mode.

Bile acids (BAs) are cholesterol metabolites with important biological functions. They undergo extensive host-gut microbial co-metabolisms during the enterohepatic circulation, creating a vast structural diversity and resulting in great challenges to separate and detect them. Based on the bioanalytical reports in the past decade, this work developed three chromatographic gradient methods to separate a total of 48 BA standards on an ethylene-bridged hybrid (BEH) C18 column and high-strength silica (HSS) T3 column and accordingly unraveled the factors affecting the separation and detection of them by liquid chromatography coupled with mass spectrometry (LC-MS). It was shown that both the acidity and ammonium levels in mobile phases reduced the electrospray ionization (ESI) of BAs as anions of [M-H]-, especially for those unconjugated ones without 12-hydroxylation. It was also found that the retention of taurine conjugates on the BEH C18 column was sensitive to the strength of formic acid and ammonium in mobile phases. By using the volatile buffers with an equivalent ammonium level as mobile phases, we comprehensively demonstrated the effects of the elution pH value on the retention behaviors of BAs on both the BEH C18 column and HSS T3 column. Based on the retention data acquired on a C18 column, we presented the ionization constants (pK a) of various BAs with the widest coverage beyond those of previous reports. When we made attempts to establish the structure-retention relationships (SRRs) of BAs, the lack of discriminative structural descriptors for BA stereoisomers emerged as the bottleneck problem. The methods and results presented in this work are especially useful for the development of reliable, sensitive, high-throughput, and robust LC-MS bioanalytical protocols for the quantitative metabolomic studies. Graphical Abstract Nonlinear curve fitting of capacity factors and elution pH value for the separation of common unconjugated bile acids.

Global and Targeted Metabolomics Evidence of the Protective Effect of Chinese Patent Medicine Jinkui Shenqi Pill on Adrenal Insufficiency after Acute Glucocorticoid Withdrawal in Rats.

Glucocorticoids are commonly used in anti-inflammatory and immunomodulatory therapies, but glucocorticoid withdrawal can result in life-threatening risk of adrenal insufficiency. Chinese patented pharmaceutical product Jinkui Shenqi pill (JKSQ) has potent efficacy on clinical adrenal insufficiency resulting from glucocorticoid withdrawal. However, the underlying molecular mechanism remains unclear. We used an animal model to study JKSQ-induced metabolic changes under adrenal insufficiency and healthy conditions. Sprague-Dawley rats were treated with hydrocortisone for 7 days with or without 15 days of JKSQ pretreatment. Sera were collected after 72 h hydrocortisone withdrawal and used for global and free fatty acids (FFAs)-targeted metabolomics analyses using gas chromatography/time-of-flight mass spectrometry and ultraperformance liquid chromatography/quadruple time-of-flight mass spectrometry. Rats without hydrocortisone treatment were used as controls. JKSQ pretreatment normalized the significant changes of 13 serum metabolites in hydrocortisone-withdrawal rats, involving carbohydrates, lipids, and amino acids. The most prominent effect of JKSQ was on the changes of FFAs and some [product FFA]/[precursor FFA] ratios, which represent estimated desaturase and elongase activities. The opposite metabolic responses of JKSQ in adrenal insufficiency rats and normal rats highlighted the "Bian Zheng Lun Zhi" (treatment based on ZHENG differentiation) guideline of TCM and suggested that altered fatty acid metabolism was associated with adrenal insufficiency after glucocorticoid withdrawal and the protective effects of JKSQ.

Metabolomics analysis reveals variation in Schisandra chinensis cetabolites from different origins.

Wu Wei Zi (Schisandra chinensis), an important herbal medicine, is mainly distributed in the northeast of China. Its phytochemical compositions, which depend on geographical origin, climatic conditions and cultural practices, may vary largely among Wu Wei Zi from different areas. In this study, we applied a comprehensive metabolite profiling approach using GC-TOF-MS, ultra-performance LC (UPLC) quadrupole TOF (QTOF) MS and inductively coupled plasma MS to systematically investigate the metabolite variations of S. chinensis from four different areas including Heilongjiang, Liaoning, Jilin, and Shanxi of China. A total of 65 primary metabolites, 35 secondary metabolites and 64 inorganic elements were identified. Several primary metabolites, including shikimic acid and tricarboxylic acid cycle intermediates, were abundant in those located in Heilongjiang, Jilin, and Liaoning. Besides, bioactive lignans are also highly abundant in those from northeastern China than those from northwestern China. Inorganic elements varied significantly among the different locations. Our results suggested that the metabolite profiling approach using GC-TOF-MS, ultra-performance LC quadrupole TOF MS, and inductively coupled plasma MS is a robust and reliable method that can be effectively used to explore subtle variations among plants from different geographical locations.

Efficient and stabilized molecular doping of hole-transporting materials driven by a cyclic-anion strategy for perovskite solar cells.

Bis(trifluoromethane)sulfonimide lithium salt (Li-TFSI) is commonly used as an effective dopant to improve the performance of the hole-transporting material (HTM) in n-i-p perovskite solar cells (PSCs). However, the ultra-hygroscopic and migratory nature of Li-TFSI leads to inferior stability of PSCs. Here, we report on a strategy to regulate the anion unit in Li-TFSI from linear to cyclic, constructing a new dopant, lithium 1,1,2,2,3,3-hexafluoropropane-1,3-disulfonimide (Li-CYCLIC), for the state-of-the-art poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] (PTAA). Mechanistic and experimental results reveal that the cyclic anion CYCLIC- exhibits stronger interaction with Li+ and PTAA˙+ compared with the linear anion TFSI-, thus significantly restraining the moisture absorption and migration of Li+ and improving the thermodynamic stability of PTAA˙+CYCLIC-. With this molecular engineering, the resulting PSCs based on Li-CYCLIC obtained an improved efficiency, along with remarkably enhanced stability, retaining 96% of the initial efficiency after over 1150 hours under continuous 1 sun illumination in an N2 atmosphere, yielding an extrapolated T 80 of over 12 000 hours. In a broader context, the proposed strategy of linear-to-cyclic doping provides substantial guidance for the subsequent advancement in the development of effective dopants for photoelectric devices.

Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with GSimp.

Missing values caused by the limit of detection or quantification (LOD/LOQ) were widely observed in mass spectrometry (MS)-based omics studies and could be recognized as missing not at random (MNAR). MNAR leads to biased statistical estimations and jeopardizes downstream analyses. Although a wide range of missing value imputation methods was developed for omics studies, a limited number of methods were designed appropriately for the situation of MNAR. To facilitate MS-based omics studies, we introduce GSimp, a Gibbs sampler-based missing value imputation approach, to deal with left-censor missing values in MS-proteomics datasets. In this book, we explain the MNAR and elucidate the usage of GSimp for MNAR in detail.

A spatially resolved single cell genomic atlas of the adult human breast.

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

Spatial charting of single-cell transcriptomes in tissues.

Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections, but do not have single-cell resolution. Here, we developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. We benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness. We then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. We performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data show that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization.

Peripheral serum metabolomic profiles inform central cognitive impairment.

The incidence of Alzheimer's disease (AD) increases with age and is becoming a significant cause of worldwide morbidity and mortality. However, the metabolic perturbation behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of 6 metabolites, glycolithocholate (GLCA), petroselinic acid, linoleic acid, myristic acid, palmitic acid, palmitoleic acid and the deoxycholate/cholate (DCA/CA) ratio, along with the dysregulation scores of 3 metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences across both brain and serum diagnostic groups (P-value < 0.05). Significant associations were observed between the levels of differential metabolites/pathways and cognitive performance, neurofibrillary tangles, and neuritic plaque burden. Metabolites abundances and personalized metabolic pathways scores were used to derive machine learning models, respectively, that could be used to differentiate cognitively impaired persons from those without cognitive impairment (median area under the receiver operating characteristic curve (AUC) = 0.772 for the metabolite level model; median AUC = 0.731 for the pathway level model). Utilizing these two models on the entire baseline control group, we identified those who experienced cognitive decline in the later years (AUC = 0.804, sensitivity = 0.722, specificity = 0.749 for the metabolite level model; AUC = 0.778, sensitivity = 0.633, specificity = 0.825 for the pathway level model) and demonstrated their pre-AD onset prediction potentials. Our study provides a proof-of-concept that it is possible to discriminate antecedent cognitive impairment in older adults before the onset of overt clinical symptoms using metabolomics. Our findings, if validated in future studies, could enable the earlier detection and intervention of cognitive impairment that may halt its progression.