Biology subjects

Goulet, L.

Publications and source records attributed to Goulet, L..

3 recordsLinked to original sources

bioRxiv2026

Instructor Perspectives on Challenging Topics in Evolution Education and Their Implications for Game-Based Learning

Evolution is a foundational framework for understanding biology, yet it remains challenging to teach and learn. Game-based learning may offer one way to support evolution instruction by helping students visualize abstract, dynamic, and difficult-to-observe processes. In this study, we interviewed undergraduate biology instructors to examine how they evaluated video games as potential tools for evolution education, including which topics they perceived as most challenging for students. We found that instructors were broadly open to using video games for evolution instruction, particularly when games could support population-level reasoning, evolutionary mechanisms, speciation and phylogeny, quantitative reasoning, and long time scales. Instructors also emphasized that games must be scientifically accurate, accessible, and aligned with course learning goals. This study contributes an instructor-centered perspective to evolution education and game-based learning research by identifying how instructors connect persistent student learning challenges with potential design priorities for educational video games. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/736808v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@d7fcd4org.highwire.dtl.DTLVardef@17caeforg.highwire.dtl.DTLVardef@c3c0aorg.highwire.dtl.DTLVardef@139e8dd_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education↗

bioRxiv2026

Leukemia stem cell expansion cultures reveal clonal drivers of leukemogenesis and therapy response

Leukemia stem cells (LSCs) contain the highest capacity for leukemia-reinitiation and therapy-resistance across all leukemic cells, but our understanding of their molecular and cellular properties remains limited due to their relative rarity and ineffective cell culture systems to maintain their purity at scale. Here, we develop Polymer-based Leukemic STem-cell Cultures (PLSTCs) and demonstrate their capacity to derive and propagate large numbers of Npm1cA/Flt3ITD acute myeloid leukemia (AML) stem cells at high purities. Compared to traditional cultures, PLSTCs show more than 1000-fold enrichment in functional LSCs based on single-cell gene expression signatures and leukemia-initiating assays. Tracing LSC clones with genomic LARRY barcodes during ex vivo expansion, we reveal that PLSTCs can sustain a diversity of self-renewing LSC states with stable, heritable transcriptional programs. Using dynamic state-fate analysis, we characterize clonal programs that are linked with enhanced ex vivo self-renewal, in vivo leukemia initiation, and therapeutic response to induction chemotherapy. LSC clones primed to resist treatment were enriched for a rare cell state that underwent a fate-switch and produced megakaryocytic-erythroid-like leukemic cells that expanded in the spleen. Targeting LSC programs through pooled CRISPR and single-cell sequencing (CROPseq) in PLSTCs, we reveal that chondroitin-sulfate synthesis is required to maintain a primitive LSC state and leukemic recovery from chemotherapy. In sum, our studies showcase the powerful application of scalable leukemic stem-cell expansion cultures and dynamic state-fate analysis of AML LSCs. We anticipate these systems will accelerate our understanding and interception of stem cell plasticity in cancer.

cancer biology↗

bioRxiv2025

CroCoDeEL: accurate control-free detection of cross-sample contamination in metagenomic data

Metagenomic sequencing provides profound insights into microbial communities, but it is often compromised by technical biases, including cross-sample contamination. This phenomenon arises when microbial content is inadvertently exchanged among concurrently processed samples, distorting microbial profiles and compromising the reliability of metagenomic data and downstream analyses. Existing detection methods often rely on negative controls, which are inconvenient and do not detect contamination within real samples. Meanwhile, strain-level bioinformatics approaches fail to distinguish contamination from natural strain sharing and lack sensitivity. To fill this gap, we introduce CroCoDeEL, a decision-support tool for detecting and quantifying cross-sample contamination. Leveraging linear modeling and a pre-trained supervised model, CroCoDeEL identifies specific contamination patterns in species abundance profiles. It requires no negative controls or prior knowledge of sample processing positions, offering improved accuracy and versatility. Benchmarks across three public datasets demonstrate that CroCoDeEL accurately detects contaminated samples and identifies their contamination sources, even at low rates (<0.1%), provided sufficient sequencing depth. Notably, we discovered critical contamination cases in highly cited studies, calling some of their results into question. Our findings suggest that cross-sample contamination is a widespread yet underexplored issue in metagenomics and emphasize the necessity of systematically integrating contamination detection into sequencing quality control.

bioinformatics↗