Biology subjects

Heidari, E.

Publications and source records attributed to Heidari, E..

8 recordsLinked to original sources

bioRxiv2025

Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data

The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current cell segmentation methods are prone to over- and under-segmentation, misassign transcripts to cells, require manual intervention, and suffer from low sensitivity and scalability. We introduce segger, a versatile graph neural network based on a heterogeneous graph representation of individual transcripts and cells, that frames cell segmentation as a transcript-to-cell link prediction task and can leverage single-cell RNA-seq information to improve transcript assignments. On multiple Xenium dataset benchmarks, segger exhibits superior sensitivity and specificity, while requiring orders of magnitude less compute time than existing methods. The user-friendly open-source software implementation has extensive documentation (https://elihei2.github.io/segger_dev/), requires little manual intervention, integrates seamlessly into existing workflows, and enables atlas-scale applications.

bioinformatics↗

bioRxiv2024

Integrated combinatorial functional genomics and spatial transcriptomics of tumors decodes genotype to phenotype relationships

Linking the complex genetic changes underlying cancer to relevant disease-phenotypes poses a challenge. Therefore, we present CHOCOLAT-G2P, a scalable approach that integrates multiplex in vivo functional genomics with spatial transcriptomics. By redeploying RNA-templated ligation probes of commercial spatial transcriptomics technology, we streamline mapping composite genetic alterations and transcriptome-wide phenotyping on the same tissue section on a single readout platform. Using this framework, we studied combinatorial effects of 8 perturbations that induce autochthonous mosaic liver tumors sampled from 256 genotypes. Interrogating 324 tumors across six [~]6x6 mm2 sections, we charted phenotypic landscapes of genotypically-defined tumor ecosystems, revealing zonation-associated hepatocellular carcinoma subclasses and associations between tumor subtypes and stromal-as well as immune-cell signatures. Further, we decoded epistasis within compound genotypes uncovering opposing roles of Vegfa and mutant Ctnnb1 to cholangiocarcinoma development. Thus, CHOCOLAT-G2P lays a foundation to decipher how combinations of alterations interact to reprogram tumor cells and their microenvironment within the holistic context of tissue and whole organisms. (https://chocolat-g2p.dkfz.de/).

cancer biology↗

bioRxiv2023

SpatialData: an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, handling uni- and multi-modal spatial omics datasets remains a challenge owing to large volumes of data, heterogeneous data types and the lack of unified spatially-aware data structures. Here, we introduce SpatialData, a framework that establishes a unified and extensible multi-platform file-format, lazy representation of larger-than-memory data, transformations, and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated via multiple vignettes, including integrative analysis on a multi-modal Xenium and Visium breast cancer study.

bioinformatics↗

bioRxiv2022

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.

bioinformatics↗

bioRxiv2022

Supervised spatial inference of dissociated single-cell data with SageNet

Spatially-resolved transcriptomics uncovers patterns of gene expression at supercellular, cellular, or subcellular resolution, providing insights into spatially variable cellular functions, diffusible morphogens, and cell-cell interactions. However, for practical reasons, multiplexed single cell RNA-sequencing remains the most widely used technology for profiling transcriptomes of single cells, especially in the context of large-scale anatomical atlassing. Devising techniques to accurately predict the latent physical positions as well as the latent cell-cell proximities of such dissociated cells, represents an exciting and new challenge. Most of the current approaches rely on an autocorrelation assumption, i.e., cells with similar transcriptomic profiles are located close to each other in physical space and vice versa. However, this is not always the case in native biological contexts due to complex morphological and functional patterning. To address this challenge, we developed SageNet, a graph neural network approach that spatially reconstructs dissociated single cell data using one or more spatial references. SageNet first estimates a gene-gene interaction network from a reference spatial dataset. This informs the structure of the graph on which the graph neural network is trained to predict the region of dissociated cells. Finally, SageNet produces a low-dimensional embedding of the query dataset, corresponding to the reconstructed spatial coordinates of the dissociated tissue. Furthermore, SageNet reveals spatially informative genes by extracting the most important features from the neural network model. We demonstrate the utility and robust performance of SageNet using molecule-resolved seqFISH and spot-based Spatial Transcriptomics reference datasets as well as dissociated single-cell data, across multiple biological contexts. SageNet is provided as an open-source python software package at https://github.com/MarioniLab/SageNet.

bioinformatics↗

bioRxiv2022

Single nuclei RNAseq stratifies multiple sclerosis patients into three distinct white matter glia responses

The lack of understanding of the cellular and molecular basis of clinical and genetic heterogeneity in progressive multiple sclerosis (MS) has hindered the search for new effective therapies. Here, to address this gap, we analysed 632,000 single nuclei RNAseq profiles of 156 brain tissue samples, comprising white matter (WM) lesions, normal appearing WM, grey matter (GM) lesions and normal appearing GM from 54 MS patients and 26 controls. We observed the expected changes in overall neuronal and glial numbers previously described within the classical lesion subtypes. We found highly cell type-specific gene expression changes in MS tissue, with distinct differences between GM and WM areas, confirming different pathologies. However, surprisingly, we did not observe distinct gene expression signatures for the classical different WM lesion types, rather a continuum of change. This indicates that classical lesion characterization better reflects changes in cell abundance than changes in cell type gene expression, and indicates a global disease effect. Furthermore, the major biological determinants of variability in gene expression in MS WM samples relate to individual patient effects, rather than to lesion types or other metadata. We identify four subgroups of MS patients with distinct WM glial gene expression signatures and patterns of oligodendrocyte stress and/or maturation, suggestive of engagement of different pathological processes, with an additional more variable regenerative astrocyte signature. The discovery of these patterns, which were also found in an independent MS patient cohort, provides a framework to use molecular biomarkers to stratify patients for optimal therapeutic approaches for progressive MS, significantly advances our mechanistic understanding of progressive MS, and highlights the need for precision-medicine approaches to address heterogeneity among MS patients.

neuroscience↗

bioRxiv2018

A Novel Workflow for Helth Survey Analysis: Results on the Yazd Health Study (YaHS)

Health surveys are one of the most prominent sources of information in medical sciences. Analysis of a health survey not only gives information about the inherent features of the population under study but also sheds light on the interactions between different health factors. Even though there has been a multitude of methodologies proposed for survey analysis, many of them are biased towards the previous hypotheses, the fact which may lead to spurious results by neglecting the whole structure of relations among the factors. Here, we proposed our novel workflow on survey analysis. By gathering the previously less-utilized graphical models on survey analysis, newly-proposed methods, and commonly-used tools of prediction and hypothesis testing our workflow provides the medical community with an end-to-end pipeline for comprehensive statistical analysis of health surveys. To demonstrate the functionality of our workflow, we validate it on the Yazd Health Study (YaHS) dataset, in which a questionnaire of 300 questions from 10000 participants associated with 40 laboratory measurements from a subset of 4010 individuals is acquired from the residents of the Yazd greater area of Iran. Many of our findings are aligned with previous medical knowledge. Most of our novel findings are surprisingly observed in the clinics, each of which is potential to raise a promising hypothesis.

bioinformatics↗

bioRxiv2018

Pin1 Regulatory miRNAs as Novel Candidates for Alzheimer’s Disease Treatment

Alzheimers disease (AD) is the sixth leading cause of death in elderly people whose pathological hallmarks include senile plaques and neurofibrillary tangles (NFTs). The tangles are composed of hyperphosphorylated tau, which is a microtubule-associated protein and its hyperphosphorylation would result in its aggregation and neural cell death. Recently, it has been shown that phosphorylated tau at Thr231 exists in two distinct cis & trans conformations, whose conversion is being mediated by Pin1 isomerase and that the cis, but not the trans, is extremely neurotoxic and drives tau hyperphosphorylation. It has been demonstrated that Pin1 inhibition reflects cis pT231-tau accumulation in neurons but its overactivation is observed in cancer stem cells. Hence, a precise Pin1 regulation is required to keep cells in healthy conditions. As miRNAs play a crucial role in fine-tuning of the gene-expression level, we hypothesized that they might regulate the Pin1 dosage. Nonetheless, the possible regulatory roles of miRNAs in progression of AD by regulating PIN1 is not well studied. We aimed to identify potential miRNAs that down-regulate PIN1 in AD. This can uncover new regulatory mechanisms that result in AD. Thus, we performed a comprehensive study of miRNAs, capable in regulating Pin1, through whole-genome meta-analysis by integrating miRNA expression profiles of 846 different biological samples, along with a systematic literature review and data mining of multiple experimental and predicted miRNA-target databases. We created a list of 56 candidates, which was then short-listed to 10 miRNAs with vigorous experimental evidence. We examined the expression patterns of these miRNAs in the AD and healthy controls and integrated mRNA and miRNA expression profiles to study possible interactions between miRNAs and Pin1. Moreover, we performed an in-silico functional analysis by integrating data of knock-in and knock-down experiments of the candidate miRNAs, and highlighted miR296-5p, miR200b, miR200c, miR140-5p, and miR874 as strong candidate Pin1 regulators. These findings would have profound implications in developing novel therapeutic strategies for AD by blocking expression of highlighted miRNAs using antagomirs.

neuroscience↗