Biology subjects

Hurtado, E.

Publications and source records attributed to Hurtado, E..

4 recordsLinked to original sources

bioRxiv2026

Minimally invasive monitoring of clonal evolution through integrated single cell and ctDNA analysis

Circulating cell-free DNA (cfDNA) offers a minimally invasive lens into tumor evolution. However, the accurate quantification of clonal composition from cfDNA remains challenging. Existing methods for clone tracking using liquid biopsies are constrained by issues such as their reliance on bulk tissue references, incomplete representations of clonal architecture or limited breadth of mutation panels. To address these limitations, we developed cfClone, a Bayesian framework that integrates single-cell whole-genome sequencing (scWGS) derived clonal structures with cfDNA whole-genome sequencing (cfDNA-WGS) data to enable high-resolution tissue-informed clonal tracking. By jointly modeling local copy-number alterations and haplotype-specific signals via Bayesian model selection and Markov chain Monte Carlo (MCMC) sampling, the algorithm yields uncertainty-aware estimates of clonal prevalence and tumour fraction (TF). Applied to longitudinal clinical cohorts, cfClone can be used to reconstruct evolutionary trajectories and uncovers clonal selection driving therapeutic resistance, including the de novo detection of emergent clonal populations. We demonstrate that cfClone achieves accurate TF estimates and circulating tumor DNA (ctDNA) detection in malignancies with varying degrees of copy-number variant (CNV) burden using semi-synthetic data. We then compare to the state of the art scWGS informed panel based approach, and demonstrate cfClone provides comparable accuracy while allowing for the tracking of more clones and detection of novel clones. Finally, we show how cfClone can be used to quantitatively track clonal dynamics in response to treatment in high grade serous ovarian cancer. Github link: https://github.com/Roth-Lab/cfclone

cancer biology↗

bioRxiv2026

Data selection choices influence the inferred movement patterns of Plasmodium sporozoites in skin

Motility of Plasmodium sporozoites (SPZs) in the skin is a key determinant of successful host infection. Earlier studies have described rapid movement of both murine and human SPZs in skin following syringe inoculation. It is typical to classify SPZ trajectories into "motile" and "immotile" and restrict the analysis of movement patterns to motile SPZs. Because criteria to define motile SPZs are dependent on the study and are often qualitative, it remains unclear if sub-selection of motile tracks introduces biases in characterization of SPZ movement in vivo. We processed imaging data (22 movies) from a recent study of movement of P. falciparum (Pf) and P. yoelii (Py) SPZ in skin. We proposed a novel metric - maximal spatial spread (MSS or S) -- that is the maximum Euclidean distance between any two recorded positions in a trajectory. We used MSS to classify SPZ trajectories as immotile (S [≤] Sthreshold) or motile (S > Sthreshold) for a given threshold value Sthreshold. Larger Sthreshold values naturally resulted in a smaller fraction of tracks classified as motile, and subsequently, in an increased overall displacement, instantaneous and mean speeds, decreased mean turning angle, and higher initial slopes of the mean squared displacement (MSD) curves. We found that at intermediate values of Sthreshold Pf SPZs had a lower average speed than Py SPZs suggesting that host environment may impact SPZ movement. Both species exhibited a small but statistically significant decline in average speed with time after inoculation but this was also dependent on the Sthreshold value. Our analysis of MSD curves and turning angle distributions suggests that both Pf and Py SPZs undergo correlated random walks - a type of Brownian walk with short-term superdiffusive displacement. By using a novel methodology of hidden Markov models (moveHMM package in R) we found that SPZ movement is best described by three movement states; however, none of these states corresponded to previously described circling gliding. Taking together, our results suggest that inference of SPZ movement patterns depends on the criteria used to define tracks as motile or immotile. Standardized preprocessing criteria are therefore important when comparing motility across Plasmodium species, experimental time points, or laboratories. Analysis of turning angle distributions and application of hidden Markov models provided additional metrics to quantify distinct modes of SPZ movement in vivo.

microbiology↗

bioRxiv2025

Using Imaris to rigorously track PET-defined sites of lung inflammation in Mycobacterium tuberculosis-exposed non-human primates

Aerosol exposure of non-human primates (NHPs) to Mycobacterium tuberculosis (Mtb) typically results in discrete sites of inflammation of the lung that is detectable by 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG)-based PET/CT scans. Such scans are often analyzed using software such as Invicro VivoQuant or OsiriX as 3D images by manual labeling sites of PET signal using 2D slices and by reporting maximal standardized uptake value (SUVmax) either of the whole lung or of individual lesions. Here we propose a pipeline for analysis of the same PET/CT scans using Imaris, a proprietary software typically used for analysis of fluorescent microscopy data. We show that by using locations of spine vertebra (denoted as "landmarks") we can align serials scans of the same animal, and by using automated (with some manual corrections) image segmentation of PET scans in 3D as "surfaces", we can accurately define location of all sites of inflammation in the lung and lung-associated thoracic lymph nodes (LNs). We show that there is an excellent correlation between individual lesions SUVmax determined by VivoQuant and maximum intensity determined by Imaris suggesting utility of this approach. Imaris also provides wealth of additional information for each of the identified lesions such as volume, location, shape, surface area, and others, and each lesion can be exported in Virtual Reality file format (.wrl) allowing for detailed and rigorous analyses of how features of these PET-defined lesions evolve over time and correlate with the outcome of infection and/or treatment.

immunology↗

bioRxiv2024

PhyClone: Accurate Bayesian reconstruction of cancer phylogenies from bulk sequencing

MotivationCancer is driven by somatic mutations that result in the expansion of genomically distinct sub-populations of cells called clones. Identifying the clonal composition of tumours and understanding the evolutionary relationships between clones is a crucial task in cancer genomics. Bulk DNA sequencing is commonly used for studying the clonal composition of tumours, but it is challenging to infer the genetic relationship between different clones due to the mixture of different cell populations. ResultsIn this work, we introduce a new probabilistic model called PhyClone that can infer clonal phylogenies from bulk sequencing data. We demonstrate the performance of PhyClone on simulated and real-world datasets and show that it outperforms previous methods in terms of accuracy and scalability. Availability and implementationSource code is available on Github at: https://github.com/Roth-Lab/PhyClone under the GPL v3.0 license. Contactaroth@bccrc.ca

bioinformatics↗