Biology subjects

Rappoport, N.

Publications and source records attributed to Rappoport, N..

14 recordsLinked to original sources

bioRxiv2025

Model-based Standardization of Correlation Coefficients Improves Multi-Omic Clustering and Biological Signal Discovery

Multi-omic data pose a particular challenge for Weighted Correlation Network Analysis (WCNA or WGCNA) due to (platform- or) batch-specific characteristics, such as resolution, accuracy, dynamic range, and sources of spurious variation. When unaccounted for, these differences can result in a bias toward single-batch clusters as well as greater sensitivity to "noisier" batches during clustering. Here we propose mitigating these effects using null models fitted separately to the bulk of analyte-analyte correlations within each batch and across each pair of batches. We then map the batch-specific null models to a standard null model, removing batch-dependent distributional differences. This approach is compatible with any correlation-based clustering approach. Since the null model represents information not captured in individual pairwise correlations, we show how to incorporate this additional information into both distance-based clustering and WCNA. For distance-based clustering, we increase distances corresponding to correlations consistent with the null model. For WCNA, we provide a new soft threshold (adjacency) function based on the likelihood of a correlation under the null model. The resulting network can be easily incorporated into the WCNA workflow. These methods are implemented in R package standardcor, and we illustrate the package on simulated data as well as an existing multi-omic dataset.

bioinformatics↗

bioRxiv2025

Deciphering the Origin and Plasticity of Circulating Endothelial Cells: A Model for Systemic Angiogenesis Programs in Health and Disease

Endothelial cells line the inner surface of blood and lymphatic vessels and play key roles in vascular function. Circulating endothelial cells (CECs) are endothelial cells found in the bloodstream, yet their origin and functional potential have not been fully elucidated. To investigate their identity, we assembled and analyzed multiple public single-cell gene expression atlases comprising 212,144 endothelial cells from 23 human tissues. Using these datasets, we identified gene modules that quantitatively resolve endothelial types and states, including arterial, capillary, venous, lymphatic, and angiogenic-state programs, along with genes that are unique to endothelial cells of specific tissues. Leveraging this knowledge to 597 CECs, which we isolated from 2.1 million circulating CD34 cells across 287 donors, we found that CECs span a wide spectrum of mature endothelial identities and tissue-specific programs, consistent with diverse vascular origins. In culture, CECs downregulate mature endothelial markers (e.g., CD34) and acquire a progenitor-like PROCR transcriptional expression. Alteration of the Notch signaling in-vitro reverses this shift, with indirect activation of the pathway inducing a capillary and angiogenic program reminiscent of mature endothelial cells. As we profiled the angiogenic program in-vivo and created an in-vitro angiogenic EC model, we sought to determine whether similar transcriptional programs are present in the context of cancer. We analyzed publicly available single-cell RNA-seq datasets from normal and malignant breast tissue, uncovering a marked enrichment of angiogenic endothelial cells not only within the tumor but, surprisingly, also in histologically normal contralateral breast tissue from breast cancer patients, pointing to a previously unrecognized systemic paraneoplastic effect. Transcriptomic overlap between these in-vivo angiogenic cells and Notch-biased cultured CEC derivatives suggests that CEC cultures may serve as an accessible human model for tumor-driven and paraneoplastic vascular remodeling. Together, our study introduces a comprehensive framework for deciphering endothelial cell identity, illuminates CEC origin and plasticity, and provides a scalable platform to study cancer-associated endothelial reprogramming.

molecular biology↗

bioRxiv2024

Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain aging is complex and accompanied by many cellular changes2-20. However, the influence that aged cells have on neighboring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in aging tissues have not yet been developed. Here, we generate spatiotemporal data at single-cell resolution for the mouse brain across lifespan, and we develop the first machine learning models based on spatial transcriptomics ( spatial aging clocks) to reveal cell proximity effects during brain aging and rejuvenation. We collect a single-cell spatial transcriptomics brain atlas of 4.2 million cells from 20 distinct ages and across two rejuvenating interventions--exercise and partial reprogramming. We identify spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Using spatial aging clocks and deep learning models, we find that T cells, which infiltrate the brain with age, have a striking pro-aging proximity effect on neighboring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating effect on neighboring cells. By developing computational tools to identify mediators of these proximity effects, we find that pro-aging T cells trigger a local inflammatory response likely via interferon-{gamma} whereas pro-rejuvenating neural stem cells impact the metabolism of neighboring cells possibly via growth factors (e.g. vascular endothelial growth factor) and extracellular vesicles, and we experimentally validate some of these predictions. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.

neuroscience↗

bioRxiv2024

Plasma proteomics in the UK Biobank reveals youthful brains and immune systems promote healthspan and longevity

Organ-derived plasma protein signatures derived from aptamer protein arrays track organ-specific aging, disease, and mortality in humans, but the robustness and clinical utility of these models and their biological underpinnings remain unknown. Here, we estimate biological age of 11 organs from 44,526 individuals in the UK Biobank using an antibody-based proteomics platform to model disease and mortality risk. Organ age estimates are associated with future onset of heart failure (heart age HR=1.83), chronic obstructive pulmonary disease (lung age HR=1.39), type II diabetes (kidney age HR=1.58), and Alzheimers disease (brain age HR=1.81) and sensitive to lifestyle factors such as smoking and exercise, hormone replacement therapy, or supplements. Remarkably, the accrual of aged organs progressively increases mortality risk while a youthful brain and immune system are uniquely associated with disease-free longevity. These findings support the use of plasma proteins for monitoring organ health and the efficacy of drugs targeting organ aging disease.

molecular biology↗

bioRxiv2023

Natural and age-related variation in circulating human hematopoietic stem cells

Hematopoietic stem and progenitor cells (HSPCs) deliver life-long multi-lineage output. However, with aging, we exhibit certain characteristic blood count changes and accumulation of clonal disorders. Better understanding of inter-individual variation in HSPC behavior is needed to understand these age-related phenomena and the transition from health to chronic and acute hematological malignancies. Here we study 627K single circulating CD34+ HSPCs (cHSPCs) from 148 healthy individuals, along with their clinical information and clonal hematopoiesis (CH) profiles, to characterize population-wide and age-related hematopoietic variability. Individuals with CH were linked with reduced frequencies of lymphocyte progenitors and higher RDW. An age-related decrease in lymphoid progenitors was observed, predominantly in males. Inter-individual transcriptional variation in expression of a Lamin-A signature and stemness gene programs were linked with aging and presence of macrocytic anemia. Based on our model for healthy cHSPC variation we construct the normal reference for cHSPC subtype frequencies. We show how compositional and expression deviations from this normal reference can robustly identify myeloid malignancies and pre-malignant states. Together, our data and methodologies present a novel resource, shedding light on various age-related hematopoietic processes, and a comprehensive normal cHSPC reference, which can serve as a tool for diagnosing and characterizing hematological disorders.

molecular biology↗

bioRxiv2023

Selecting Covariates for Genome-Wide Association Studies

The choice of which covariates to include in a Genome-Wide Association Study (GWAS) is important since it affects the ability to detect true association signal of variants, to correct for confounders and avoid false positives, and the running time of the analysis. Commonly used covariates include age, sex, genotyping batches, genotyping array type, as well as an arbitrary number of Principal Components (PCs) used to adjust for population structure. Despite the importance of this issue, there is no consensus or clear guidelines for the right choice of covariates. Therefore, studies typically employ heuristics for their choice with no clear justification. Here, we explore the dependence of the GWAS analysis results on the choice of covariates for a wide range of quantitative and binary human phenotypes. We propose guidelines for covariates choice based on the phenotypes type (quantitative vs. disease), the heritability, and the disease prevalence, with the goal of maximizing the statistical power to detect true associations and fit accurate polygenic scores while avoiding spurious associations and minimizing computation time. We analyze 36 traits in the UK-Biobank dataset. We show that the genotype batch and assessment center can be safely removed as covariates, thus significantly reducing the GWAS computational burden for these traits.

genetics↗

bioRxiv2022

Integration of Gene Expression and DNA Methylation Data Across Different Experiments

Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to solve it. Here, we present INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples. To enable integration, INTEND learns a predictive model between the two omics by training on multi-omic data measured on the same set of samples. In comprehensive testing on eleven TCGA cancer datasets spanning 4329 patients, INTEND achieves significantly superior results compared to four state-of-the-art integration algorithms. We also demonstrate INTENDs ability to uncover connections between DNA methylation and the regulation of gene expression in the joint analysis of two lung adenocarcinoma single-omic datasets from different sources. INTENDs data-driven approach makes it a valuable multi-omic data integration tool. The code for INTEND is available at https://github.com/Shamir-Lab/INTEND.

bioinformatics↗

bioRxiv2021

ProteinBERT: A universal deep-learning model of protein sequence and function

Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically designed for proteins. Our pretraining scheme consists of masked language modeling combined with a novel task of Gene Ontology (GO) annotation prediction. We introduce novel architectural elements that make the model highly efficient and flexible to very large sequence lengths. The architecture of ProteinBERT consists of both local and global representations, allowing end-to-end processing of these types of inputs and outputs. ProteinBERT obtains state-of-the-art performance on multiple benchmarks covering diverse protein properties (including protein structure, post translational modifications and biophysical attributes), despite using a far smaller model than competing deep-learning methods. Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data. Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert.

bioinformatics↗

bioRxiv2020

MONET: Multi-omic patient module detection by omic selection

Recent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present Monet (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. Monet discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most extant multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures using Bayesian statistics. We tested Monet extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that Monet compares favorably with other multi-omic clustering methods. We demonstrate Monets biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that Monet is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that Monet is a valuable tool that can provide complementary results to those provided by extant algorithms for multi-omic analysis.

bioinformatics↗

bioRxiv2020

Coordinated Interaction: A model and test for globally signed epistasis in complex traits

Interactions between genetic variants - epistasis - is pervasive in model systems and can profoundly impact evolutionary adaption, population disease dynamics, genetic mapping, and precision medicine efforts. In this work we develop a model for structured polygenic epistasis, called Coordinated Interaction (CI), and prove that several recent theories of genetic architecture fall under the formal umbrella of CI. Unlike standard polygenic epistasis models that assume interaction and main effects are independent, in the CI model, sets of SNPs broadly interact positively or negatively, on balance skewing the penetrance of main genetic effects. To test for the existence of CI we propose the even-odd (EO) test and prove it is calibrated in a range of realistic biological models. Applying the EO test in the UK Biobank, we find evidence of CI in 14 of 26 traits spanning disease, anthropometric, and blood categories. Finally, we extend the EO test to tissue-specific enrichment and identify several plausible tissue-trait pairs. Overall, CI is a new dimension of genetic architecture that can capture structured, systemic interactions in complex human traits.

genetics↗

bioRxiv2018

Tracing diagnosis trajectories over millions of inpatients reveal an unexpected association between schizophrenia and rhabdomyolysis

While it has been technically feasible to create longitudinal representations of individual health at a nationwide scale, the use of these techniques to identify novel disease associations for the risk stratification of patients has had limited success. Here, we created a large-scale US longitudinal disease network of traced readmission patterns (i.e., disease trajectories), merging data from over 10.4 million inpatients from 350 California hospitals through the Healthcare Cost and Utilization Project between 1980 and 2010. We were able to create longitudinal representations of disease progression mapping over 300 common diseases, including the well-known complication of heart failure after acute myocardial infarction. Surprisingly, out of these generated disease trajectories, we discovered an unknown association between schizophrenia, a chronic mental disorder, and rhabdomyolysis, a rare disease of muscle breakdown. It was found that 92 of 3674 patients (2.5%) with schizophrenia were readmitted for rhabdomyolysis (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95] P-value 9.54E-15), which has a general population incidence of 1 in 10,000. We validated this association using independent electronic health records from over 830,000 patients treated over seven years at the University of California, San Francisco (UCSF) medical center. A case review of 29 patients at UCSF who were treated for schizophrenia and who went on to develop rhabdomyolysis demonstrated that the majority of cases (62%) are idiopathic, which suggests a biological connection between these two diseases. Together, these findings demonstrate the power of using public disease registries in combination with electronic medical records to discover novel disease associations.\n\nOne Sentence SummaryBased on the longitudinal health records from millions of California inpatient discharges, we created a temporal network that enabled us to understand statewide patterns of hospital readmissions, which led to the novel finding that hospitalization for schizophrenia is significantly associated with rehospitalization for rhabdomyolysis.

bioinformatics↗

bioRxiv2018

NEMO: Cancer subtyping by integration of partial multi-omic data

Motivation: Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients.\n\nResults: We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multiomics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO outperformed nine state-of-the-art multi-omics clustering algorithms on full data and on imputed partial data. On some of the partial data tests, PVC, a multiview algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization.\n\nAvailability: Code for NEMO and for reproducing all NEMO results in this paper is in github.\n\nContact: rshamir@tau.ac.il\n\nSupplementary information: Supplementary data are available online.

bioinformatics↗

bioRxiv2018

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

High throughput experimental methods developed in recent years have been used to collect large biomedical omics datasets. Clustering of such datasets has proven invaluable for biological and medical research, and helped reveal structure in data from several domains. Such analysis is often based on investigation of a single omic. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges. Here we review algorithms for multi-omics clustering, and discuss key issues in applying these algorithms. Our review covers methods developed specifically for multi-omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types.\n\nIn addition, using cancer data from TCGA, we perform an extensive benchmark spanning ten different cancer types, providing the first systematic benchmark comparison of leading multi-omics and multiview clustering algorithms. The results highlight several key questions regarding the use of single-vs. multi-omics, the choice of clustering strategy, the power of generic multi-view methods and the use of approximated p-values for gauging solution quality. Due to the rapidly increasing use of multi-omics data, these issues may be important for future progress in the field.

bioinformatics↗

bioRxiv2017

Creating ethnicity-specific reference intervals for lab tests from EHR data

The results of clinical lab tests are an essential component of medical decision-making. To guide interpretation, test results are returned with reference intervals defined by the range in which 95% of values occur in healthy individuals. Clinical laboratories often set their own reference intervals to accommodate local population and instruments variations. This approach is costly and can be biased. We describe a novel data-driven method for using electronic health record data to extract healthy patients information to define reference intervals. We found that the distributions of many clinical lab tests differ among self-identified racial and ethnic groups (SIREs) in healthy patients. Finally, we derived SIRE-specific reference intervals and provide evidence that these intervals have clinical prognostic value. Specifically, we show that for two lab tests, serum creatinine level and hemoglobin A1C, SIRE-specific reference intervals are more predictive for need for dialysis and development type 2 diabetes than existing reference intervals.\n\nOne Sentence SummaryA novel method for defining population-specific reference intervals of common clinical laboratory tests from electronical health records has better prognostic value than existing reference intervals.

bioinformatics↗