Biology subjects

Olivares-Chauvet, P.

Publications and source records attributed to Olivares-Chauvet, P..

4 recordsLinked to original sources

bioRxiv2026

NovaClone: A Network-Based Algorithm for Clonal and Subclonal Genotyping of Barcoded Transgene Integrations

Single-cell lineage tracing technologies are providing new and powerful ways to interrogate the evolution and divergence of cell populations in cancer, development, and other contexts. A key initial step in any such analysis is the grouping of cells into clonal populations, based on clone-level marks. Unfortunately, clone calling is prone to technical effects due to sequencing errors, missing data, multiplets, background noise, and accidental sharing of clonal barcodes between unrelated clones (homoplasy). We present NovaClone, a principled algorithm for hierarchical clone calling that is broadly applicable to all current tracing technologies, including both static barcoding and the more recent evolving tracers. We benchmark NovaClone on simulated and real data to show that it outperforms the current solutions in terms of both quality and speed, thereby helping to mitigate one of the most prevalent problems with single-cell lineage tracing. To complement NovaClone, we introduce a suite of algorithm-agnostic quality control metrics to evaluate clone calls when ground truth is not available. NovaClone and the associated QCs are available through the open source Python package nova-clone.

genomics↗

bioRxiv2025

Lineage origin and microenvironment shape neuroblastoma transcriptional state and plasticity

Neuroblastoma, a neural-crest-derived malignancy of the peripheral nervous system, is a devastating pediatric disease characterized by high intra- and intertumoral heterogeneity. While several tumor expression programs correlate with patient outcome, the developmental and contextual determinants of their emergence remain incompletely understood. Here, we systematically dissected neuroblastoma transcriptional heterogeneity and measured how tumor expression programs are associated with lineage history and developmental context. To achieve this, we combined single-cell transcriptomics with high-throughput lineage tracing and tumor cell transplantation in zebrafish models of MYCN-driven neuroblastoma. We identified developmental cell-state-associated programs that align with sympathoadrenal development, as well as general cellular-process programs. Clonal analysis showed that developmental programs are preferentially clone-associated, whereas cellular-process programs are more context-responsive. Transplantation into an embryonic environment reshaped the activation of several initially clone-associated programs, consistent with developmentally determined state reconfiguration and/or selection of state-competent cells. Together, our results show that lineage history and developmental context jointly shape tumor-state architecture in vivo.

cancer biology↗

bioRxiv2020

Variability of an early developmental cell population underlies stochastic laterality defects

Embryonic development seemingly proceeds with almost perfect precision. However, it is largely unknown how much underlying microscopic variability is compatible with normal development. Here, we quantified embryo-to-embryo variability in vertebrate development, by studying cell number variation in the zebrafish endoderm. We noticed that the size of a sub-population of the endoderm, the dorsal forerunner cells (which later form the left-right organizer), exhibits significantly more embryo-to-embryo variation than the rest of the endoderm. We found that, when incubated at elevated temperature, the frequency of left-right laterality defects is increased drastically in embryos with a low number of dorsal forerunner cells. Furthermore, we observed that these fluctuations have a large stochastic component among fish of the same genetic background. Hence, a stochastic variation in early development leads to a remarkably strong macroscopic phenotype. These fluctuations appear to be associated with maternal effects in the specification of the dorsal forerunner cells.

developmental biology↗

bioRxiv2017

SHAMAN: bin-free randomization, normalization and screening of Hi-C matrices

Genome wide chromosome conformation capture (Hi-C) is used to interrogate contact frequencies among genomic elements at multiple scales and intensities, ranging from high frequency interactions among proximal regulatory elements, through specific long-range loops between insulator binding sites and up to rare and transient cis- and trans-chromosomal contacts. Visualization and statistical analysis of Hi-C data is made difficult by the extreme variation in the background frequencies of chromosomal contacts between elements at short and long genomic distances. Here we introduce SHAMAN for performing Hi-C analysis at dynamic scales, without predefined resolution, and while minimizing biases over very large datasets. Algorithmically, we devise a Markov Chain Monte Carlo-like procedure for randomizing contact matrices such that coverage and contact distance distributions are preserved. We combine this strategy with bin-free assessment of contact enrichment using a K-nearest neighbor approach. We show how to use the new method for visualizing contact hotspots and for quantifying differential contacts in matching Hi-C maps. We demonstrate how contact preferences among regulatory elements, including promoters, enhancers and insulators can be assessed with minimal bias by comparing pooled empirical and randomized matrices. Full support for our methods is available in a new software package that is freely available.

bioinformatics↗