Biology subjects

Jones, M. G.

Publications and source records attributed to Jones, M. G..

At least 19 recordsLinked to original sources

bioRxiv2026

Pycea: a scverse toolkit for single-cell lineage tracing analysis

Lineage tracing enables reconstruction of cellular histories in development, regeneration, and disease, but integrating lineage data into single-cell analysis workflows remains challenging. We introduce Pycea, a Python toolkit for lineage analysis and visualization, and TreeData, an extension of AnnData that stores lineage trees alongside cellular measurements. TreeData is compatible with Scanpy and other scverse tools, enabling integrated analysis without converting between formats. Pycea provides scalable methods for tree visualization, clade identification, distance comparisons, ancestral state inference, and lineage statistics. This software is open source and available at https://github.com/YosefLab/pycea.

bioinformatics↗

bioRxiv2026

Murine osteosarcoma recapitulates the driver landscape and genomic complexity of osteosarcoma evolution in humans

Osteosarcoma (OS) genomes are characterized by complex genomic rearrangements (CGRs) that drive genomic instability and clonal diversification early in tumor evolution. As a result, OS tumors display high inter-patient variability, which has hindered molecular stratification and targeted therapeutic development. To study genomic complexity in OS and credential a genetically engineered mouse model of the disease (Sp7-Cre Trp53fl Rb1fl), we performed high-depth and multi-region whole genome sequencing (WGS) of 35 tumor samples from 24 mice. Similar to human OS, the murine OS tumors (mOS) had a high number of somatic structural variants (158 per tumor) with low tumor mutational burden of single nucleotide variants (0.87 mutations/MB). CGRs were identified in 63% (15/24) of mOS cases, most frequently affecting chromosome 15 (33%, 8/24 mice) and resulting in Myc amplification in 6 mice, ranging from 5 to 104 copies. Myc amplification was verified with DNA FISH, long-read sequencing and gene expression data, which revealed examples of Myc amplification in both extrachromosomal circular DNA (ecDNA) and in derivative chromosomes generated by CGRs. PTEN loss occurred frequently (59% 12/22 mice), and contributed to osteosarcomagenesis, as demonstrated by tumor initiation with in vivo CRISPR/Cas9-mediated deletion experiments (2 mice). Together, these results demonstrate that a preclinical model of osteosarcoma can generate the genomic heterogeneity and complexity of the human disease, thereby facilitating research into mechanisms of tumor initiation and drivers of progression and relapse.

cancer biology↗

bioRxiv2026

scAmp analyzes focal gene amplifications at single-cell resolution

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common driver of tumor progression and is associated with acquired drug resistance and poor patient survival. While whole genome sequencing (WGS) studies have revealed the landscape of genes amplified on ecDNA in tumors, it remains challenging to study the subclonal heterogeneity and functional (e.g., transcriptomic) consequences of ecDNA on tumors. To address this, we introduce scAmp: a probabilistic algorithm for detecting and analyzing ecDNA from single-cell datasets. We demonstrate scAmps improved accuracy over WGS approaches on well-characterized cell-lines and its applicability to clinical histopathology. We further showcase scAmp by analyzing 73 patient tumors profiled with single-cell ATAC-seq, where we analyze the subclonal evolution of ecDNA+ subclones and identify the effect of ecDNA amplifications on the chromatin accessibility landscape of cancer cells. Together, we anticipate that scAmp will broadly enable further studies - both retrospective and prospective - that dissect critical questions of how ecDNA affect cancer cells and the tumors in which they reside.

cancer biology↗

bioRxiv2025

Tahoe-x1: Scaling Perturbation-Trained Single-CellFoundation Models to 3 Billion Parameters

Foundation models have transformed natural language processing and computer vision, yet their potential in single-cell biology--particularly for complex diseases such as cancer-- remains underexplored. We present Tahoe-x1 (Tx1), a family of perturbation-trained single-cell foundation models with up to 3 billion parameters. Tx1 is pretrained on large-scale single-cell transcriptomic datasets, including the Tahoe-100M perturbation compendium, and fine-tuned for cancer-relevant tasks. Through architectural optimizations, data loader refinements, and efficient training strategies, Tx1 achieves 3-30x higher compute efficiency than prior implementations of cell-state models. Tx1 jointly learns representations of genes, cells, and compounds using a masked-expression generative objective that incorporates a drug token, enabling flexible adaptation to diverse downstream applications. We evaluate Tx1 across four key disease-relevant benchmarks: (1) prediction of overall and context-specific gene essentiality, (2) identification of genes contributing to the hallmarks of cancer, (3) cell-type classification, and (4) prediction of perturbation responses in held-out cellular contexts. Tx1 achieves state-of-the-art performance across all tasks. We release pretrained checkpoints, training code, and evaluation workflows to accelerate the development of perturbation-trained single-cell foundation models for applications in precision oncology and beyond. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=52 SRC="FIGDIR/small/683759v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@aa33d5org.highwire.dtl.DTLVardef@310a38org.highwire.dtl.DTLVardef@195f204org.highwire.dtl.DTLVardef@143f7e3_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Genetic elements promote retention of extrachromosomal DNA in cancer cells

Extrachromosomal DNA (ecDNA) is a prevalent and devastating form of oncogene amplification in cancer1,2. Circular megabase-sized ecDNAs lack centromeres and segregate stochastically during cell division3-6 yet persist over many generations. EcDNAs were first observed to hitchhike on mitotic chromosomes into daughter cell nuclei over 40 years ago with unknown mechanism3,7. Here we identify a family of human genomic elements, termed retention elements, that tether episomes to mitotic chromosomes to increase ecDNA transmission to daughter cells. We develop Retain-seq, a genome-scale assay that reveals thousands of human retention elements conferring generational persistence to heterologous episomes. Retention elements comprise a select set of CpG-rich gene promoters and act additively. Live-cell imaging and chromatin conformation capture show that retention elements physically interact with mitotic chromosomes at regions which are mitotically bookmarked by transcription factors and chromatin proteins, intermolecularly recapitulating promoter-enhancer interactions. Multiple retention elements are co-amplified with oncogenes on individual ecDNAs in human cancers and shape their sizes and structures. CpG-rich retention elements are focally hypomethylated; targeted cytosine methylation abrogates retention activity and leads to ecDNA loss, suggesting that methylation-sensitive interactions modulate episomal DNA retention. These results highlight the DNA elements and regulatory logic of mitotic ecDNA retention. Amplifications of retention elements promote the maintenance of oncogenic ecDNA across generations of cancer cells, revealing the principles of episome immortality intrinsic to the human genome.

cancer biology↗

bioRxiv2025

High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer

Charting the spatiotemporal dynamics of cell fate determination in development and disease is a long-standing objective in biology. Here we present the design, development, and extensive validation of PEtracer, a prime editing-based, evolving lineage tracing technology compatible with both single-cell sequencing and multimodal imaging methodologies to jointly profile cell state and lineage in dissociated cells or while preserving cellular context in tissues with high spatial resolution. Using PEtracer coupled with MERFISH spatial transcriptomic profiling in a syngeneic mouse model of tumor metastasis, we reconstruct the growth of individually-seeded tumors in vivo and uncover distinct modules of cell-intrinsic and cell-extrinsic factors that coordinate tumor growth. More generally, PEtracer enables systematic characterization of cell state and lineage relationships in intact tissues over biologically-relevant temporal and spatial scales.

molecular biology↗

bioRxiv2025

EcDNA-borne PVT1 fusion stabilizes oncogenic mRNAs

Extrachromosomal DNA (ecDNA) amplifications are prevalent drivers of human cancers. We show that ecDNAs exhibit elevated structural variants leading to gene fusions that produce oncogene fusion transcripts. The long noncoding RNA (lncRNA) gene PVT1 is the most recurrent structural variant across cancer genomes, with PVT1-MYC fusions arising most frequently on ecDNA. PVT1 exon 1 is the predominant 5 partner fused to MYC or other oncogenes on the 3 end. Mechanistic studies demonstrate that PVT1 exon 1 confers enhanced RNA stability for fusion transcripts, which requires PVT1 exon 1 interaction with SRSF1 protein. Genetic rescue of MYC-addicted cancer models and isoform-specific single-cell RNA sequencing of tumors reveal that PVT1-MYC better supports MYC dependency and better activates MYC target genes in vivo. Thus, the mutagenic landscape of ecDNA contributes to genome instability and generates chimeric fusions of lncRNA and mRNA genes, selecting PVT1 5 region as a stabilizer of oncogene mRNAs.

cancer biology↗

bioRxiv2025

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling

Building predictive models of the cell requires systematically mapping how perturbations reshape each cells state, function, and behavior. Here, we present Tahoe-100M, a giga-scale single-cell atlas of 100 million transcriptomic profiles measuring how each of 1,100 small-molecule perturbations impact cells across 50 cancer cell lines. Our high-throughput Mosaic platform, composed of a highly diverse and optimally balanced "cell village", reduces batch effects and enables parallel profiling of thousands of conditions at single-cell resolution at an unprecedented scale. As the largest single-cell dataset to date, Tahoe-100M enables artificial-intelligence (AI)-driven models to learn context-dependent functions, capturing fundamental principles of gene regulation and network dynamics. Although we leverage cancer models and pharmacological compounds to create this resource, Tahoe-100M is fundamentally designed as a broadly applicable perturbation atlas and supports deeper insights into cell biology across multiple tissues and contexts. By publicly releasing this atlas, we aim to accelerate the creation and development of robust AI frameworks for systems biology, ultimately improving our ability to predict and manipulate cellular behaviors across a wide range of applications.

genomics↗

bioRxiv2024

NoButter: An R package for reducing transcript dis-persion in CosMx Spatial Molecular Imaging Data

MotivationAdvances in spatial transcriptomics technologies at single-cell resolution have high-lighted the need for innovative quality assessment approaches and improved analytical tools. Imaging-based spatial transcriptomics technologies, such as the CosMx Spatial Molecular Imager (SMI), provide the location and abundance of transcripts through multifocal imaging. Optical sections (or Z-slices) form a Z-stack that represents the tissue depth. Transcript dispersion can be observed across these Z-slice and introduce considerable levels of technical noise to the data that can negatively impact downstream analysis. Package FunctionalityNoButter is an R package designed to evaluate transcript dispersion in CosMx SMI spatial transcriptomics data. Using the raw data, the transcript distribution is assessed for each Z-slice of a Z-stack across multiple fields of views (FOVs). To systematically identify transcript dispersion, the percentage of transcripts located outside cell boundaries is calculated. Z-slices exhibiting high levels of transcript dispersion can be excluded, while high-confidence transcripts are preserved. Usage ScenarioTo demonstrate the functionalities of NoButter, spatial transcriptomics data was generated using the CosMx SMI for lymph node tissue, a lung sample, and two triple-negative breast cancers (TNBCs). Use cases illustrate substantial transcript dispersion in optical planes closer to the glass slide. In these Z-slices, on average, an additional 10% of the transcripts were discarded using NoButter. Cleaning such Z-slices with high dispersion rates reduces technical noise and improves the overall quality of the spatial transcriptomics data. AvailabilityThe package can be accessed at https://github.com/cancerbioinformatics/NoButter.

bioinformatics↗

bioRxiv2024

Extrachromosomal DNA driven oncogene spatial heterogeneity and evolution in glioblastoma

Oncogene amplification on extrachromosomal DNA (ecDNA) is strongly associated with treatment resistance and shorter survival for patients with cancer, including patients with glioblastoma. The non-chromosomal inheritance of ecDNA during cell division is a major contributor to intratumoral genetic heterogeneity. At present, the spatial dynamics of ecDNA, and the impact on tumor evolutionary trajectories, are not well understood. Here, we investigate the spatial-temporal evolution of ecDNA and its clinical impact by analyzing tumor samples from 94 treatment-naive human IDH-wildtype glioblastoma patients. We developed a spatial-temporal computational model of ecDNA positive tumors ( SPECIES) that integrates whole-genome sequencing, multi-region DNA FISH, and nascent RNAscope, to provide unique insight into the spatial dynamics of ecDNA evolution. Random segregation in combination with positive selection of ecDNAs induce large, predictable spatial patterns of cell-to-cell ecDNA copy number variation that are highly dependent on the oncogene encoded on the circular DNA. EGFR ecDNAs often reach high mean copy number (mean of 50 copies per tumor cell), are under strong positive selection (mean selection coefficient, s > 2) and do not co-amplify other oncogenes on the same ecDNA particles. In contrast, PDGFRA ecDNAs have lower mean copy number (mean of 15 copies per cell), are under weaker positive selection and frequently co-amplify other oncogenes on the same ecDNA. Evolutionary modeling suggests that EGFR ecDNAs often accumulate prior to clonal expansion. EGFR structural variants, including vIII and c-terminal deletions are under strong positive selection, are found exclusively on ecDNA, and are intermixed with wild-type EGFR ecDNAs. Simulations show EGFRvIII ecDNA likely arises after ecDNA formation in a cell with high wild-type EGFR copy number (> 10) before the onset of the most recent clonal expansion. This remains true even in cases of co-selection and co-amplification of multiple oncogenic ecDNA species in a subset of patients. Overall, our results suggest a potential time window in which early ecDNA detection may provide an opportunity for more effective intervention. HighlightsO_LIecDNA is the most common mechanism of focal oncogene amplification in IDHwt glioblastoma. C_LIO_LIEGFR and its variants on ecDNA are particularly potent, likely arising early in tumor development, providing a strong oncogenic stimulus to drive tumorigenesis. C_LIO_LIWild-type and variant EGFR ecDNA heteroplasmy (co-occurrence) is common with EGFRvIII or c-terminal deletions being derived from EGFR wild-type ecDNA prior to the most recent clonal expansion. C_LIO_LITumors with ecDNA amplified EGFR versus PDGFRA exhibit different evolutionary trajectories. C_LIO_LISPECIES model can infer spatial evolutionary dynamics of ecDNA in cancer. C_LIO_LIA delay between ecDNA accumulation and subsequent oncogenic mutation may give a therapeutic window for early intervention. C_LI

cancer biology↗

bioRxiv2024

Spatiotemporal lineage tracing reveals the dynamic spatial architecture of tumor growth and metastasis

Tumour progression is driven by dynamic interactions between cancer cells and their surrounding microenvironment. Investigating the spatiotemporal evolution of tumours can provide crucial insights into how intrinsic changes within cancer cells and extrinsic alterations in the microenvironment cooperate to drive different stages of tumour progression. Here, we integrate high-resolution spatial transcriptomics and evolving lineage tracing technologies to elucidate how tumour expansion, plasticity, and metastasis co-evolve with microenvironmental remodelling in a Kras;p53-driven mouse model of lung adenocarcinoma. We find that rapid subclonal expansion contributes to a hypoxic, immunosuppressive, and fibrotic microenvironment that is associated with the emergence of pro-metastatic cancer cell states. Furthermore, metastases arise from spatially-confined subclones of primary tumours and remodel the distant metastatic niche into a fibrotic, collagen-rich microenvironment. Together, we present a comprehensive dataset integrating spatial assays and lineage tracing to elucidate how sequential changes in cancer cell state and microenvironmental structures cooperate to promote tumour progression.

cancer biology↗

bioRxiv2024

Enhancer activation from transposable elements in extrachromosomal DNA

Extrachromosomal DNA (ecDNA) drives oncogene amplification and intratumoral heterogeneity in aggressive cancers. While transposable element (TE) reactivation is common in cancer, its role on ecDNA remains unexplored. Here, we map the 3D architecture of MYC-amplified ecDNA in colorectal cancer cells and identify 68 ecDNA-interacting elements (EIEs)--genomic loci enriched for TEs that are frequently integrated onto ecDNA. We focus on an L1M4a1#LINE/L1 fragment co-amplified with MYC, which functions only in the ecDNA amplified context. Using CRISPR-CATCH, CRISPR interference, and reporter assays, we confirm its presence on ecDNA, enhancer activity, and essentiality for cancer cell fitness. These findings reveal that repetitive elements can be reactivated and co-opted as functional rather than inactive sequences on ecDNA, potentially driving oncogene expression and tumor evolution. Our study uncovers a mechanism by which ecDNA harnesses repetitive elements to shape cancer phenotypes, with implications for diagnosis and therapy.

genetics↗

bioRxiv2024

Tree reconstruction guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining

CRISPR-Cas9 based lineage tracing technologies have enabled the reconstruction of single-cell phylogenies from transcriptional readouts. However, developing tree-reconstruction algorithms with theoretical guarantees in this setting is challenging. In this work, we derive a reconstruction algorithm with theoretical guarantees using Neighbor-Joining (NJ) on distances that are moment-matched to estimate the true tree distances. We develop a series of tools to analyze this algorithm and prove its theoretical guarantees. When the parameters of the data generating process are known and there is no missing data, our results align with established results from common evolutionary models, such as Cavender-Farris-Neyman and Jukes-Cantor. However, to account for the realistic case where the parameters of the data generating process are not known and there is missing data, we develop new theory that shows for the first time that it is still possible to obtain reconstruction guarantees in the CRISPR-Cas9 case and in other models of evolution. Empirically, we show on both simulated lineage tracing data and on real data from a mouse model of lung cancer the improved performance of our method as compared to the traditional use of NJ.

bioinformatics↗

bioRxiv2024

CoRAL accurately resolves extrachromosomal DNA genomestructures with long-read sequencing

Extrachromosomal DNA (ecDNA) is a central mechanism for focal oncogene amplification in cancer, occurring in approximately 15% of early stage cancers and 30% of late-stage cancers. EcDNAs drive tumor formation, evolution, and drug resistance by dynamically modulating oncogene copy-number and rewiring gene-regulatory networks. Elucidating the genomic architecture of ecDNA amplifications is critical for understanding tumor pathology and developing more effective therapies. Paired-end short-read (Illumina) sequencing and mapping have been utilized to represent ecDNA amplifications using a breakpoint graph, where the inferred architecture of ecDNA is encoded as a cycle in the graph. Traversals of breakpoint graph have been used to successfully predict ecDNA presence in cancer samples. However, short-read technologies are intrinsically limited in the identification of breakpoints, phasing together of complex rearrangements and internal duplications, and deconvolution of cell-to-cell heterogeneity of ecDNA structures. Long-read technologies, such as from Oxford Nanopore Technologies, have the potential to improve inference as the longer reads are better at mapping structural variants and are more likely to span rearranged or duplicated regions. Here, we propose CoRAL (Complete Reconstruction of Amplifications with Long reads), for reconstructing ecDNA architectures using long-read data. CoRAL reconstructs likely cyclic architectures using quadratic programming that simultaneously optimizes parsimony of reconstruction, explained copy number, and consistency of long-read mapping. CoRAL substantially improves reconstructions in extensive simulations and 9 datasets from previously-characterized cell-lines as compared to previous short-read-based tools. As long-read usage becomes wide-spread, we anticipate that CoRAL will be a valuable tool for profiling the landscape and evolution of focal amplifications in tumors.

cancer biology↗

bioRxiv2023

Disparate pathways for extrachromosomal DNA biogenesis and genomic DNA repair

Oncogene amplification on extrachromosomal DNA (ecDNA) is a pervasive driver event in cancer, yet our understanding of how ecDNA forms is limited. Here, we couple a CRISPR-based method for induction of ecDNA with extensive characterization of newly formed ecDNA to examine ecDNA biogenesis. We find that DNA circularization is efficient, irrespective of 3D genome context, with formation of a 1 Mb and 1.8 Mb ecDNA both reaching 15%. We show non-homologous end joining and microhomology mediated end joining both contribute to ecDNA formation, while inhibition of DNA-PKcs and ATM have opposing impacts on ecDNA formation. EcDNA and the corresponding chromosomal excision scar form at significantly different rates and respond differently to DNA-PKcs and ATM inhibition. Taken together, our results support a model of ecDNA formation in which double strand break ends dissociate from their legitimate ligation partners prior to joining of illegitimate ends to form the ecDNA and excision scar. SIGNIFICANCEOur study harnesses a CRISPR-based method to examine ecDNA biogenesis, uncovering efficient circularization between DSBs. ecDNAs and their corresponding chromosomal scars can form via NHEJ or MMEJ, but the ecDNA and scar formation processes are distinct. Based on our findings, we establish a mechanistic model of excisional ecDNA formation.

cancer biology↗

bioRxiv2023

Coordinated inheritance of extrachromosomal DNA species in human cancer cells

The chromosomal theory of inheritance has dominated human genetics, including cancer genetics. Genes on the same chromosome segregate together while genes on different chromosomes assort independently, providing a fundamental tenet of Mendelian inheritance. Extrachromosomal DNA (ecDNA) is a frequent event in cancer that drives oncogene amplification, dysregulated gene expression and intratumoral heterogeneity, including through random segregation during cell division. Distinct ecDNA sequences, herein termed ecDNA species, can co-exist to facilitate intermolecular cooperation in cancer cells. However, how multiple ecDNA species within a tumor cell are assorted and maintained across somatic cell generations to drive cancer cell evolution is not known. Here we show that cooperative ecDNA species can be coordinately inherited through mitotic co-segregation. Imaging and single-cell analyses show that multiple ecDNAs encoding distinct oncogenes co-occur and are correlated in copy number in human cancer cells. EcDNA species are coordinately segregated asymmetrically during mitosis, resulting in daughter cells with simultaneous copy number gains in multiple ecDNA species prior to any selection. Computational modeling reveals the quantitative principles of ecDNA co-segregation and co-selection, predicting their observed distributions in cancer cells. Finally, we show that coordinated inheritance of ecDNAs enables co-amplification of specialized ecDNAs containing only enhancer elements and guides therapeutic strategies to jointly deplete cooperating ecDNA oncogenes. Coordinated inheritance of ecDNAs confers stability to oncogene cooperation and novel gene regulatory circuits, allowing winning combinations of epigenetic states to be transmitted across cell generations.

cancer biology↗

bioRxiv2021

Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth, expansion to neighboring and distal tissues, and therapeutic resistance. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma which enabled us to track tumor evolution from single transformed cells to metastatic tumors at unprecedented resolution. We found that loss of the initial, stable alveolar-type2-like state was accompanied by transient increase in plasticity. This was followed by adoption of distinct fitness-associated transcriptional programs which enable rapid expansion and ultimately clonal sweep of rare, stable subclones capable of metastasizing to distant sites. Finally, we showed that tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates tumor progression by creating novel evolutionary paths. Overall, our study elucidates the hierarchical nature of tumor evolution, and more broadly enables the in-depth study of tumor progression.

cancer biology↗

bioRxiv2021

PhyloVision: Interactive Software for Integrated Analysis of Single-Cell Transcriptomic and PhylogeneticData

Recent advances in CRISPR-Cas9 engineering and single-cell assays have enabled the simultaneous measurement of single-cell transcriptomic and phylogenetic profiles. However, there are few computational tools enabling users to integrate and derive insight from a joint analysis of these two modalities. Here, we describe PhyloVision: an open source software for interactively exploring data from both modalities and for identifying and interpreting heritable gene modules whose concerted expression are associated with phylogenetic relationships. PhyloVision provides a feature-rich, interactive, and shareable web-based report for investigating these modules, while also supporting several other data and meta-data exploration capabilities. We demonstrate the utility of PhyloVision using a published dataset of metastatic lung adenocarcinoma cells, whose phylogeny was resolved using a CRISPR/Cas9-based lineage-tracing system. Together, we anticipate that PhyloVision and the methods it implements will be a useful resource for scalable and intuitive data exploration for any assay that simultaneously measures cell state and lineage.

bioinformatics↗