Biology subjects

Missarova, A.

Publications and source records attributed to Missarova, A..

7 recordsLinked to original sources

bioRxiv2026

Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

Understanding the role of tertiary lymphoid structures (TLS) is crucial in non-small cell lung cancer (NSCLC), as they are associated with patient prognosis and treatment outcomes. Specific cellular ecosystems that originate anti-tumor activity or predict immunotherapy response remain poorly characterized. To this end, we developed a high-resolution, multimodal spatial atlas jointly profiling transcriptomics, proteomics, and histology to characterize TLS maturation in NSCLC alongside secondary lymph organs as a baseline. Using this atlas, we proposed a pathologist-in-the-loop framework that combines a variational graph autoencoder (VGAE) with diffusion pseudotime to refine human expert annotations and characterize TLS maturation. These spatial molecular representations were extended to H&E whole-slide images via a vision transformer-based foundation model. Next, we resolved cellular composition, spatial organization, and cell-cell interactions within these data and defined two divergent spatial ecosystems. Clinical evidence suggests that these ecosystems are associated with distinct patient outcomes: a mature germinal center niche with favorable prognoses, and a tumor-macrophage-fibroblast niche with unfavorable prognoses. In summary, our work decodes key components of TLS heterogeneity, identifies hallmark spatial patterns involved in NSCLC adaptive immunity, and provides a framework for translating spatial omics insights into clinical applications.

genomics↗

bioRxiv2024

Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states

Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to consider entire tissue ecosystems, simultaneously considering many different interacting cells. Here, we tackle this challenge by generating patient-level representations derived from multi-cellular expression context measured with scRNA-seq of tissues. We develop PaSCient, a novel model that employs a multi-level representation learning paradigm and provides importance scores at the individual cell and gene levels for fine-grained analysis across multiple cell types and gene programs characteristic of a given disease. We apply PaSCient to learn a disease model across a large-scale scRNA-seq atlas of 24.3 million cells from over 5,000 patients. Comprehensive and rigorous benchmarking demonstrates the superiority of PaSCient in disease classification and its multiple downstream applications, including dimensionality reduction, gene/cell type prioritization, and patient subgroup discovery.

bioinformatics↗

bioRxiv2023

Sensitive cluster-free differential expression testing.

Comparing molecular features, including the identification of genes with differential expression (DE) between conditions, is a powerful approach for characterising disease-specific phenotypes. When testing for DE in single-cell RNA sequencing data, current pipelines first assign cells into discrete clusters (or cell types), followed by testing for differences within each cluster. Consequently, the sensitivity and specificity of DE testing are limited and ultimately dictated by the granularity of the cell type annotation, with discrete clustering being especially suboptimal for continuous trajectories. To overcome these limitations, we present miloDE - a cluster-free framework for differential expression testing. We build on the Milo approach, introduced for differential cell abundance testing, which leverages the graph representation of single-cell data to assign relatively homogenous, neighbouring cells into overlapping neighbourhoods. We address key differences between differential abundance and expression testing at the level of neighbourhood assignment, statistical testing, and multiple testing correction. To illustrate the performance of miloDE we use both simulations and real data, in the latter case identifying a transient haemogenic endothelia-like state in chimeric mouse embryos lacking Tal1 as well as uncovering distinct transcriptional programs that characterise changes in macrophages in patients with Idiopathic Pulmonary Fibrosis. miloDE is available as an open-source R package at https://github.com/MarioniLab/miloDE.

bioinformatics↗

bioRxiv2021

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.

The problem of selecting targeted gene panels that capture maximum variability encoded in scRNA-sequencing data has become of great practical importance. scRNA-seq datasets are increasingly being used to identify gene panels that can be probed using alternative molecular technologies, such as spatial transcriptomics. In this context, the number of genes that can be probed is an important limiting factor, so choosing the best subset of genes is vital. Existing methods for this task are limited by either a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cell types. We resolve this by introducing an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. We demonstrate, using a variety of metrics and diverse datasets, that our approach outperforms existing strategies, and can not only resolve cell types but also more subtle cell state differences. Our approach is available as an open source, easy-to-use, documented R package (https://github.com/MarioniLab/geneBasisR).

bioinformatics↗

bioRxiv2020

Highly multiplexed spatially resolved gene expression profiling of mouse organogenesis

Transcriptional and epigenetic profiling of single-cells has advanced our knowledge of the molecular bases of gastrulation and early organogenesis. However, current approaches rely on dissociating cells from tissues, thereby losing the crucial spatial context that is necessary for understanding cell and tissue interactions during development. Here, we apply an image-based single-cell transcriptomics method, seqFISH, to simultaneously and precisely detect mRNA molecules for 387 selected target genes in 8-12 somite stage mouse embryo tissue sections. By integrating spatial context and highly multiplexed transcriptional measurements with two single-cell transcriptome atlases we accurately characterize cell types across the embryo and demonstrate how spatially-resolved expression of genes not profiled by seqFISH can be imputed. We use this high-resolution spatial map to characterize fundamental steps in the patterning of the midbrain-hindbrain boundary and the developing gut tube. Our spatial atlas uncovers axes of resolution that are not apparent from single-cell RNA sequencing data - for example, in the gut tube we observe early dorsal-ventral separation of esophageal and tracheal progenitor populations. In sum, by computationally integrating high-resolution spatially-resolved gene expression maps with single-cell genomics data, we provide a powerful new approach for studying how and when cell fate decisions are made during early mammalian development.

developmental biology↗

bioRxiv2018

Budding yeast complete DNA replication after chromosome segregation begins

To faithfully transmit genetic information, cells must replicate their entire genome before division. This is thought to be ensured by the temporal separation of replication and chromosome segregation. Here we show that in a substantial fraction of unperturbed yeast cells, DNA replication finishes during anaphase, late in mitosis. High cyclin-Cdk activity inhibits replication in metaphase, and the decrease in cyclin-Cdk activity during mitotic exit allows DNA replication to finish at difficult-to-replicate regions. Replication during late mitosis correlates with elevated mutation rates, including copy number variation. Thus, yeast cells temporally overlap replication and chromosome segregation during normal growth, possibly allowing cells to maximize population-level growth rate while simultaneously exploring greater genetic space.\n\nOne Sentence SummaryCompletion of DNA replication is coupled to downregulation of Cyclin-Dependent Kinase during mitotic exit.

cell biology↗

bioRxiv2018

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell variation in proliferation, drug resistance and mutation outcome

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we developed a high-throughput automated microscopy assay and used it to quantify the impact of deleting >1,500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondria state - but not content - varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.

genomics↗