Biology subjects

Lazareva, O.

Publications and source records attributed to Lazareva, O..

6 recordsLinked to original sources

bioRxiv2025

A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow defined cellular trajectories across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cellular trajectory regionalises tumours into distinct tissue niches and manifests in a molecularly conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to map cancer cell states jointly with clones in situ, we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

cancer biology↗

bioRxiv2023

HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling

Single-cell DNA-sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds on an accessible modification of the 10X Genomics platform for scATAC and multiome profiling. In applications to human cell models and primary tissue, we demonstrate the feasibility to detect rare clones and we combine the assay with combinatorial indexing to profile over 16,000 cells.

cancer biology↗

bioRxiv2022

TF-Prioritizer: a java pipeline to prioritize condition-specific transcription factors

BackgroundEukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic data sets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multi-modal data sets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., ChIP-seq, ATAC-seq, or DNase-seq) and RNA-seq data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results. ResultsWe developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multi-modal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE data sets for cell lines K562 and MCF-7, including twelve histone modification ChIP-seq as well as ATAC-seq and DNase-seq datasets, where we observe and discuss assay-specific differences. ConclusionTF-Prioritizer accepts ATAC-seq, DNase-seq, or ChIP-seq and RNA-seq data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

bioinformatics↗

bioRxiv2022

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

Gene regulation is frequently altered in diseases in unique and patient-specific ways. Hence, personalized strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not scale well as they often require recomputing the entire network per sample. Moreover, they do not account for clinically important confounding factors such as age, sex, or treatment history. Finally, a user-friendly implementation for the analysis and interpretation of such net-works is missing. We present DysRegNet, a method for inferring patient-specific regulatory alterations (dysregulations) from bulk gene expression profiles. We compared DysRegNet to SSN, a well-known sample-specific network approach. We demonstrate that both SSN and DysRegNet produce interpretable and biologically meaningful networks across various cancer types. In contrast to SSN, DysRegNet can scale to arbitrary sample numbers and highlights the importance of confounders in network inference, revealing an age-specific bias in gene regulation in breast cancer. DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package), and analysis results for eleven TCGA cancer types are available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).

bioinformatics↗

bioRxiv2021

A Systematic Comparison of Differential Analysis Methods for CyTOF Data

Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist. These tools either reduce the data distribution to medians, discarding valuable information, or have underlying assumptions that may not hold for all expression patterns. Here, we systematically evaluated existing and novel approaches for differential expression analysis on real and simulated CyTOF data. We found that methods using median marker expressions compute fast and reliable results when the data is not strongly zero-inflated. Methods using all data detect changes in strongly zero-inflated markers, but partially suffer from overprediction or cannot handle big datasets. We present a new method, CyEMD, based on calculating the Earth Movers Distance between expression distributions that can handle strong zero-inflation without being too sensitive. Additionally, we developed CYANUS, a user-friendly R Shiny App allowing the user to analyze cytometry data with state-of-the-art tools, including well-performing methods from our comparison. A public web interface is available at https://exbio.wzw.tum.de/cyanus/.

bioinformatics↗

bioRxiv2020

BiCoN: Network-constrained biclustering of patients and omics data

MotivationUnsupervised learning approaches are frequently employed to identify patient subgroups and biomarkers such as disease-associated genes. Thus, clustering and biclustering are powerful techniques often used with expression data, but are usually not suitable to unravel molecular mechanisms along with patient subgroups. To alleviate this, we developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. ResultsOur analyses of non-small cell lung and breast cancer gene expression data demonstrate that BiCoN clusters patients in agreement with known cancer subtypes while discovering gene subnetworks pointing to functional differences between these subtypes. Furthermore, we show that BiCoN is robust to noise and batch effects and can distinguish between high and low load of tumor-infiltrating leukocytes while identifying subnetworks related to immune cell function. In summary, BiCoN is a powerful new systems medicine tool to stratify patients while elucidating the responsible disease mechanism. AvailabilityPyPI package: https://pypi.org/project/bicon Web interface: https://exbio.wzw.tum.de/bicon Contactolga.lazareva@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗