Biology subjects

Tamir, Y.

Publications and source records attributed to Tamir, Y..

4 recordsLinked to original sources

bioRxiv2025

Context-dependent spatial multicellular network motifs for single-cell spatial biology

The clinical state of diseased tissue is caused by complex intercellular processes that go beyond pairwise cell-cell interactions and are difficult to infer due to the combinatorial explosion of such high-dimensionality. We present context-dependent identification of spatial motifs (CISM), a two-step method to identify local cell structures associated with a disease state in single cell spatial data. First, for each tissue, CISM enumerates structures of enriched reoccurring multicellular patterns that define modular motifs in the multicellular network. Second, discriminative motifs are selected according to the context - their presence in patients at different clinical disease states. By applying CISM, we show that modular structures composed of as little as 3-5 cells and their relative spatial arrangement can encode differences in clinical disease states in cohorts of triple-negative breast cancer (TNBC) and melanoma patients. Machine learning validation indicated that discriminative motifs outperform state-of-the-art methods for disease state prediction while enabling interpretation of which interactions in what spatial context are associated with these predictions. CISM-derived discriminative motifs may define an intermediate spatial scale of abstraction and modularity in multicellular organization and function with broad applicability in the domain of spatial single cell omics and beyond.

systems biology↗

bioRxiv2024

Data-modeling the interplay between single cell shape, single cell protein expression, and tissue state

Changes in cell shape are fundamentally involved in signaling, intracellular organization, function, and intercellular interactions within tissues, in health and disease. Investigating the interplay between cell shape and protein expression was limited, until recently, by the number of proteins that can be imaged simultaneously or by population averaging. We combined spatial multiplexed single cell imaging and machine learning to systematically investigate the intricate relationships between cell shape and protein expression in the context of heterogeneous human cells in their native state in human tissue samples in situ. Our analysis established a universal bi-directional link between the cells shape and its protein expression across different cell types, diseases, and disease states in human tissues, enabling new applications. Machine learning interpretability showed that the contribution of shape features to a prediction can potentially infer new protein functions. Unbiased screening of the links between all pairs consisting of one protein and one cell type identified a subpopulation of large p53-positive tumor cells across two cancers. Ultimately, inclusion of single cell shape properties enhanced Graph Neural Network disease state prediction. Our results open the door to unraveling the intricate connections between protein expression at the single cell level, cell shape, tissue organization, and tissue state in a physiological context.

cell biology↗

bioRxiv2024

Quantification of beta cell carrying capacity in prediabetes

Prediabetes, a subclinical state of high glucose, carries a risk of transition to diabetes. One cause of prediabetes is insulin resistance, which impairs the ability of insulin to control blood glucose. However, many individuals with high insulin resistance retain normal glucose due to compensation by enhanced insulin secretion by beta cells. Individuals seem to differ in their maximum compensation level, termed beta cell carrying capacity, such that low carrying capacity is associated with a higher risk of prediabetes and diabetes. Carrying capacity has not been quantified using a mathematical model and cannot be estimated directly from measured glucose and insulin levels in patients, unlike insulin resistance and beta cell function which can be estimated using HOMA-IR and HOMA-B formula. Here we present a mathematical model of beta cell compensation and carrying capacity, and develop a new formula called HOMA-C to estimate it from glucose and insulin measurements. HOMA-C estimates the maximal potential beta cell function of an individual, rather than the current beta cell function. We test this approach using longitudinal cohorts of prediabetic people, finding 10-fold variation in carrying capacity. Low carrying capacity is associated with higher risk of transitioning to diabetes. We estimate the timescales of beta cell compensation and insulin resistance using large datasets, showing that, unlike previous mathematical models, the new model can explain the slow rise in glucose over decades. Our mathematical understanding of beta cell carrying capacity may help to assess the risk of prediabetes in each individual.

systems biology↗

bioRxiv2022

The predictive value of double-stranded RNA for A-to-I editing detection

Structured AbstractO_ST_ABSMotivationC_ST_ABSAdenosine-to-inosine (A-to-I) RNA editing, a crucial reaction for many processes that contribute to transcriptome plasticity, is both widely common across the transcriptome and difficult to predict due to a lack of distinctive genomic characteristics that can be obtained and analyzed computationally. An exception to this is the secondary structure of RNA molecules, which has been shown to have a major impact on the selectivity and specificity of the enzymes responsible for A-to-I editing. Yet, this information is rarely used for the task of editing site prediction. ResultsHere, we demonstrated the value of using base-pairing probabilities of RNA nucleotides to classify genomic sites as A-to-I RNA editing sites, using large-scale truth data which we compiled and make available for use in training future models. Our analysis suggests that the span of four bases from -2 (upstream) to +1 (downstream) of a putative editing site is most informative in this regard. A classifier trained on base-pairing probabilities alone performed with a positive predictive value (PPV) of 0.68, a negative predictive value (NPV) of 0.64, and an area under the receiver operating characteristic curve (AUC) of 0.71. By identifying structure-related features that are informative for detecting A-to-I RNA editing sites and quantifying their predictive value, this work advances our understanding of A-to-I editing determinants. AvailabilityAll source codes and data are available at https://github.com/Ally-s-Lab/P-BEP

bioinformatics↗