Biology subjects

Ivleva, E.

Publications and source records attributed to Ivleva, E..

4 recordsLinked to original sources

bioRxiv2026

A lifespan single-cell atlas of the human developing hippocampus benchmarks familial Alzheimer's disease brain organoids.

Familial Alzheimer's disease (fAD) is an early-onset form of AD caused by autosomal-dominant variants in APP, PSEN1, or PSEN2, with PSEN1 accounting for most genetically defined cases [1]. The hippocampus is among the earliest and most severely affected brain regions in AD [2,3]. Human induced pluripotent stem cell (iPSC)-derived brain organoids recapitulate key features of early human brain development and provide a tractable model for studying how fAD mutations perturb neurodevelopmental processes [4]. However, their interpretation is complicated by heterogeneous regional identity, variable maturation state, and cell-type composition across protocols [5,6]. Existing single-cell studies of human hippocampus cover prenatal [7] and postnatal [8-10] stages but do not provide a continuous developmental reference. By elevating the atlas approach in utilizing single-cell RNA-sequencing data, we obtain standardized information on the organoid cell class and type composition and maturation states. Here, we constructed the Human Developing Hippocampus Atlas (HuDeHA), an integrated single-cell reference comprising 658,059 cells spanning post-conceptional week 3 to 15.3 years, and used it to benchmark iPSC-derived brain organoids carrying PSEN1 E280A which is associated with fAD in a large Colombian population. Reference-based mapping revealed altered cellular composition in PSEN1 E280A organoids, including reduced radial glia and increased neural crest-derived neurons. These changes were accompanied by cross-lineage transcriptional alterations, including broad upregulation of the ventral patterning factor MEIS2 and reduced expression of the {beta}-binding protein transthyretin (TTR) in choroid-plexus and ependymal-associated populations. Reconstructed neuronal-lineage trajectories showed a shift toward mature states in PSEN1 E280A organoids. Together, these findings establish HuDeHA as a resource for developmental benchmarking of hippocampus-relevant organoid systems and describe cell-lineage-specific developmental changes in PSEN1 E280A organoids that may inform interpretation of early cellular alterations in fAD.

neuroscience↗

bioRxiv2026

Quantitative assessment of cell fate commitment in single-cell transcriptomics using scCS

Cell fate trajectory inference is one of the key downstream methods in single-cell RNA-sequencing data analysis. Multiple existing tools allow studying such and help identify continuous cell fate dynamics, important genes along the trajectory, and reconstruct the transcriptional states a cell passes to its estimated final state. These methods have been essential to study various biological systems, yet cell fate by itself currently presents mostly qualitative analysis, and existing tools do not allow to directly quantify the fate-related parameters, including commitment, transition speed, fate affinity and entropy. Such quantifications may be performed through multiple parameters and result in better understanding and description of cell fates. We present scCS (single-cell Commitment Scoring), a scverse-friendly Python framework for this problem. scCS introduces Discounted Future-Fate Propagation (DFFP), which models the source transition graph as a geometrically stopped random walk that can reach endpoint anchors or stop unresolved, with a user-defined finite expected graph horizon. For each cell, the resulting probabilities are separated into total fate reach, relative affinity among reached fates, entropy-based fate specificity and reach-supported resolved commitment, while Signed Ordering Flux independently quantifies local progression. scCS also provides endpoint-anchor, graph-coverage and horizon-sensitivity diagnostics, an instantaneous local-direction mode, standardized visualizations, gene-level analyses and replicate-aware comparisons across experimental conditions. Applications to pancreatic endocrinogenesis and neural crest-Schwann-cell differentiation illustrate the general framework. scCS converts an explicit biological fate hypothesis into auditable cell-, population- and replicate-level quantities without redefining the source dynamics.

bioinformatics↗

bioRxiv2026

AI-based separation of malignant cell- and microenvironment-specific gene expression from bulk RNA sequencing enhances biomarker interpretation

Bulk RNA sequencing (RNA-seq)-based gene expression analysis is a promising tool for personalized cancer diagnostics, disease monitoring, and treatment decision-making. However, its clinical utility is limited by interference from non-malignant tumor microenvironment cells, which can dominate transcript data in low-purity tumors. While cell deconvolution methods like Kassandra can predict digital cell percentages from bulk RNA-seq, approaches for delineating the gene expression contribution of tumor compartments remain limited. To overcome this limitation, we developed Helenus, a machine-learning-based tool that separates gene expression between malignant and non-malignant cells. Trained on over 200 million synthetic RNA profiles representing diverse tumor types and purities, Helenus demonstrated high accuracy in separating gene expression origin. Helenus also uncovered true genomic-RNA correlations such as copy number alterations and the expression of therapeutic antibody-drug conjugate targets specifically on tumor cells. Helenus provides critical insights into tumor biology and immunotherapy response by precisely identifying biomarker expressions, paving the way for more effective personalized cancer care. SignificanceHelenus extracts gene expression profiles of cancerous and non-cancerous compartments of tumor biopsies from bulk RNA-seq data, enabling the determination of how the expression of specific genes affects malignancy and tumor immunity.

cancer biology↗

bioRxiv2017

Common variants of NRXN1, LRP1B and RORA are associated with increased ventricular volumes in psychosis - GWAS findings from the B-SNIP deep phenotyping study

Schizophrenia, Schizoaffective, and Bipolar Disorders share common illness traits, intermediate phenotypes and a partially overlapping polygenic basis. We performed GWAS on deep phenotyping data, including structural MRI and DTI, clinical, and behavioral scales from 1,115 cases and controls. Significant associations were observed with two cerebrospinal fluid volumes: the temporal horn of left lateral ventricle was associated with NRXN1, and the volume of the cavum septum pellucidum was associated with LRP1B and RORA. Both volumes were associated with illness. Suggestive associations were observed with local gyrification indices, fractional anisotropy and age at onset. The deep phenotyping approach allowed unexpected genetic sharing to be found between phenotypes, including temporal horn of left lateral ventricle and age at onset.

genetics↗