Biology subjects

Purvis, J. E.

Publications and source records attributed to Purvis, J. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Non-Genetic Mechanisms of Fractional Resistance to Abemaciclib in Dedifferentiated Liposarcoma.

Dedifferentiated liposarcoma is a rare mesenchymal malignancy driven by amplification of chromosome 12q13-15, which includes the oncogenes CDK4 and MDM2. CDK4 amplification provides a rationale for targeted therapy with CDK4/6 inhibitors, and abemaciclib has shown the most durable activity reported to date in this disease. Clinical responses, however, are incomplete and often transient, and the cellular features that allow tumor cells to continue proliferating during treatment are not well understood. To address this gap, we performed multiplexed single-cell imaging to quantify 17 cell-cycle regulators in both dedifferentiated liposarcoma cell line Lipo246 and surgically resected primary human cells exposed to abemaciclib. Both models contained a subpopulation of cells that retained phosphorylated retinoblastoma protein, a marker of cell proliferation, at the highest abemaciclib doses. These fractionally resistant cells were defined by selective enrichment of cyclin-dependent kinase 2 (CDK2), cyclin B1, and phosphorylated ribosomal protein S6 (pS6), and showed enhanced sensitivity to the CDK2 inhibitor, tagtociclib. Together, these findings reveal nongenetic cell cycle plasticity as a mechanism of escape from CDK4/6 inhibition in dedifferentiated liposarcoma and nominate CDK2 and the PI3K-mTOR pathway as candidate targets for combination therapy.

cancer biology↗

bioRxiv2025

TRIP13 protects pancreatic cancer cells against intrinsic and therapy-induced DNA replication stress

Oncogene activation in normal untransformed cells induces DNA replication stress and creates a dependency on DNA Damage Response (DDR) mechanisms for cell survival. Different oncogenic stimuli signal via distinct mechanisms in every cancer setting. The DDR is also pathologically re-programmed and deployed in diverse ways in different cancers. Because mutant KRAS is the driver oncogene in 90% of Pancreatic Ductal Adenocarcinomas (PDAC), here we have investigated DDR mechanisms by which KRAS-induced DNA replication stress is tolerated in normal human pancreatic epithelial cells (HPNE). Using a candidate screening approach, we identify TRIP13 as a KRASG12V-induced mRNA that is also expressed at high levels in PDAC relative to normal tissues. Using genetic and pharmacological tools, we show that TRIP13 is necessary to sustain ongoing DNA synthesis and viability specifically in KRASG12V-expressing cells. TRIP13 promotes survival of KRASG12V-expressing HPNE cells in a Homologous Recombination (HR)-dependent manner. KRASG12V-expressing HPNE cells lacking TRIP13 acquire hallmark HR-deficiency (HRD) phenotypes including sensitivity to inhibitors of Trans-Lesion Synthesis (TLS) and Poly-ADP Ribose Polymerase (PARP). Established PDAC cell lines are also sensitized to intrinsic DNA damage and therapy-induced genotoxicity following TRIP13-depletion. Taken together our results expose TRIP13 as an attractive new and therapeutically-tractable vulnerability of KRAS-mutant PDAC.

cancer biology↗

bioRxiv2024

Multiplexed single-cell imaging reveals diverging subpopulations with distinct senescence phenotypes during long-term senescence induction.

Cellular senescence is a phenotypic state that contributes to the progression of age-related disease through secretion of pro-inflammatory factors known as the senescence associated secretory phenotype (SASP). Understanding the process by which healthy cells become senescent and develop SASP factors is critical for improving the identification of senescent cells and, ultimately, understanding tissue dysfunction. Here, we reveal how the duration of cellular stress modulates the SASP in distinct subpopulations of senescent cells. We used multiplex, single-cell imaging to build a proteomic map of senescence induction in human epithelial cells induced to senescence over the course of 31 days. We map how the expression of SASP proteins increases alongside other known senescence markers such as p53, p21, and p16INK4a. The aggregated population of cells responded to etoposide with an accumulation of stress response factors over the first 11 days, followed by a plateau in most proteins. At the single-cell level, however, we identified two distinct senescence cell populations, one defined primarily by larger nuclear area and the second by higher protein concentrations. Trajectory inference suggested that cells took one of two discrete molecular paths from unperturbed healthy cells, through a common transitional subpopulation, and ending at the discrete terminal senescence phenotypes. Our results underscore the importance of using single-cell proteomics to identify the mechanistic pathways governing the transition from senescence induction to a mature state of senescence characterized by the SASP.

cell biology↗

bioRxiv2023

Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells

The CDK4/6 inhibitor palbociclib blocks cell cycle progression in ER+/HER2- breast tumor cells. Although these drugs have significantly improved patient outcomes in metastatic breast cancers, a small percentage of tumor cells continues to divide in the presence of palbociclib--a phenomenon we refer to as fractional resistance. It is critical to understand the cellular mechanisms underlying fractional resistance because the precise percentage of resistant cells in patient tissue is a strong predictor of clinical outcome. Here, we hypothesize that fractional resistance arises from cell-to-cell differences in core cell cycle regulators that allow a subset of cells to escape CDK4/6 inhibitor therapy. We used multiplex, single-cell imaging to identify fractionally resistant tumor cells both in a cell culture model of ER+/HER2- breast cancer as well as live primary tumor cells resected from a patient. We found that tumor cells capable of proliferating in the presence of palbociclib showed both expected (e.g., CDK2, E2F1) and unexpected (e.g., Cdt1, p21, cyclin B1) shifts in core cell cycle regulators. Notably, resistant cells in both tumor models showed premature enrichment of the G1 regulators E2F1 and CDK2 protein and, unexpectedly, the G2/M regulator cyclin B1 just before cell cycle entry, suggesting that resistant cells may use noncanonical mechanisms to overcome CDK4/6 inhibition. Using computational data integration and trajectory inference approaches, we show how plasticity in cell cycle regulators gives rise to alternate cell cycle "paths" that allow individual ER+/HER2- tumor cells to escape palbociclib treatment. Understanding drivers of cell cycle plasticity, and how to eliminate resistant cell cycle paths, could lead to improved cancer therapies targeting fractionally resistant cells to improve patient outcomes.

cancer biology↗

bioRxiv2023

Feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or noisy and irrelevant features (e.g. measurements with low signal-to-noise ratio) can mask the underlying trajectory of study and hinder inference. Here, we present DELVE (dynamic selection of locally covarying features), an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that recapitulates cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference, and instead models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of the cell cycle and cellular differentiation, we demonstrate that DELVE selects features that more accurately characterize cell populations and improve the recovery of cell type transitions. This feature selection framework provides an alternative approach for improving trajectory inference and uncovering co-variation amongst features along a biological trajectory. DELVE is implemented as an open-source python package and is publicly available at: https://github.com/jranek/delve.

bioinformatics↗

bioRxiv2023

Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA-sequencing Data Using Graph Neural Networks

AO_SCPLOWBSTRACTC_SCPLOWWith the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

bioinformatics↗

bioRxiv2022

Single-cell dynamics of core pluripotency factors in human pluripotent stem cells

The human transcription factors OCT4, SOX2, and NANOG form a core signaling network critical for maintaining stem cell pluripotency and self-renewal potential. The spatiotemporal expression dynamics of these pluripotency factors throughout differentiation is unclear, limiting our understanding of stem cell fate decisions. Here, we combined CRISPR/Cas9-mediated gene editing with microraft array technology to generate human embryonic stem cell lines with endogenously tagged fluorophores for OCT4, SOX2, and NANOG. Fluorescence time-lapse imaging revealed that pluripotent stem cells show gastrulation-like patterning without direct chemical induction. Directed differentiation to the three primary germ layers--endoderm, mesoderm, and ectoderm--revealed distinct spatiotemporal patterns of SOX2 and NANOG expression in single cells. Finally, we captured dynamic changes in cell morphology during ectoderm differentiation corresponding to the formation of neural rosettes. This study provides a robust method for generating live-cell reporters in human stem cells and describes the single-cell dynamics of human pluripotency factors during differentiation.

developmental biology↗

bioRxiv2022

Mre11 liberates cGAS from nucleosome sequestration during tumorigenesis

Oncogene-induced replication stress generates endogenous DNA damage that activates cGAS/STING-mediated innate immune signaling and tumor suppression1-3. However, the mechanism for cGAS activation by endogenous DNA damage remains enigmatic, particularly given the constitutive inhibition of cGAS by high-affinity histone acidic patch (AP) binding4-10. Here we report an in vivo CRISPR screen that identified the DNA double strand break sensor Mre11 as a suppressor of mammary tumorigenesis induced by Myc overexpression and p53 deficiency. Mre11 antagonizes Myc-induced proliferation through cGAS/STING activation. Direct binding of the Mre11-Rad50-Nbn (MRN) complex to nucleosomes displaces cGAS from AP sequestration, which is required for DNA damage-induced cGAS mobilization and activation by cytosolic DNA. Mre11 is thereby essential for cGAS activation in response to oncogenic stress, cytosolic DNA transfection, and ionizing radiation. Furthermore, we show Mre11-dependent cGAS activation suppresses Myc-induced proliferation through ZBP1/RIPK3/MLKL-mediated necroptosis. In human triple-negative breast cancer, ZBP1 downregulation correlates with increased genome instability, decreased immune infiltration, and poor patient prognosis. These findings establish Mre11 as a critical link between DNA damage and cGAS activation that regulates tumorigenesis through ZBP1-dependent necroptosis. One-sentence summaryMre11 is required for cGAS activation during oncogenic stress and promotes ZBP1-dependent necroptosis.

cell biology↗

bioRxiv2022

The molecular architecture of cell cycle arrest

The cellular decision governing the transition between proliferative and arrested states is crucial to the development and function of every tissue. While the molecular mechanisms that regulate the proliferative cell cycle are well established, we know comparatively little about what happens to cells as they diverge into cell cycle arrest. We performed hyperplexed imaging of 49 cell cycle effectors to obtain a map of the molecular architecture that governs cell cycle exit and progression into reversible ("quiescent") and irreversible ("senescent") arrest states. Using this map, we found multiple points of divergence from the proliferative cell cycle; identified stress-specific states of arrest; and resolved the molecular mechanisms governing these fate decisions, which we validated by single-cell, time-lapse imaging. Notably, we found that cells can exit into senescence from either G1 or G2; however, both subpopulations converge onto a single senescent state with a G1-like molecular signature. Cells can escape from this "irreversible" arrest state through the upregulation of G1 cyclins. This comprehensive map provides a first glimpse of the overall organization of cell proliferation and arrest.

cell biology↗

bioRxiv2022

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark eight integration approaches on eight datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. This work provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

bioinformatics↗

bioRxiv2021

A proximal-to-distal survey of healthy adult human small intestine and colon epithelium by single-cell transcriptomics

Background and AimsSingle-cell transcriptomics offer unprecedented resolution of tissue function at the cellular level, yet studies analyzing healthy adult human small intestine and colon are sparse. Here, we present single-cell transcriptomics covering the duodenum, jejunum, ileum, and ascending, transverse, and descending colon from 3 humans. Methods12,590 single epithelial cells from three independently processed organ donors were evaluated for organ-specific lineage biomarkers, differentially regulated genes, receptors, and drug targets. Analyses focused on intrinsic cell properties and capacity for response to extrinsic signals along the gut axis across different humans. ResultCells were assigned to 25 epithelial lineage clusters. Human intestinal stem cells (ISCs) are not specifically marked by many murine ISC markers. Lysozyme expression is not unique to human Paneth cells (PCs), and PCs lack expression of expected niche-factors. BEST4+ cells express NPY and show maturational differences between SI and colon. Tuft cells possess a broad ability to interact with the innate and adaptive immune systems through previously unreported receptors. Some classes of mucins, hormones, cell-junction, and nutrient absorption genes show unappreciated regional expression differences across lineages. Differential expression of receptors and drug targets across lineages reveals biological variation and potential for variegated responses. ConclusionsOur study identifies novel lineage marker genes; covers regional differences; shows important differences between mouse and human gut epithelium; and reveals insight into how the epithelium responds to the environment and drugs. This comprehensive cell atlas of the healthy adult human intestinal epithelium resolves likely functional differences across anatomical regions along the gastrointestinal tract and advances our understanding of human intestinal physiology.

physiology↗

bioRxiv2021

The Consequences of Differential Origin Licensing Dynamics

MCM complexes are loaded onto chromosomes to license DNA replication origins in G1 phase of the cell cycle, but it is not yet known how mammalian MCM complexes are adequately distributed to both euchromatin and heterochromatin. To address this question, we combined time-lapse live-cell imaging with fixed cell immunofluorescence imaging of single human cells to quantify the relative rates of MCM loading in heterochromatin and euchromatin at different times within G1. We report here that MCM loading in euchromatin is faster than in heterochromatin in very early G1, but surprisingly, heterochromatin loading accelerates relative to euchromatin loading in middle and late G1. These different loading dynamics require ORCA-dependent differences in ORC distribution during G1. A consequence of heterochromatin origin licensing dynamics is that cells experiencing a truncated G1 phase from premature cyclin E expression enter S phase with under-licensed heterochromatin, and DNA damage accumulates preferentially in heterochromatin in the subsequent S/G2 phase. Thus G1 length is critical for sufficient MCM loading, particularly in heterochromatin, to ensure complete genome duplication and to maintain genome stability.

molecular biology↗

bioRxiv2021

Cyclin F drives proliferation through SCF-dependent degradation of the retinoblastoma-like tumor suppressor p130/RBL2

Cell cycle gene expression programs fuel proliferation and are dysregulated in many cancers. The retinoblastoma-family proteins, RB, p130/RBL2 and p107/RBL1, coordinately repress cell cycle gene expression, inhibiting proliferation and suppressing tumorigenesis. Ubiquitin-dependent protein degradation is essential to cell cycle control, and numerous proliferative regulators, tumor suppressors, and oncoproteins are ubiquitinated. However, little is known about the role of ubiquitin signaling in controlling RB-family proteins. A systems genetics analysis of several hundred CRISPR/Cas9 loss-of-function screens suggested the potential regulation of the RB-network by cyclin F, a substrate recognition receptor for the SCF family of E3 ligases. We demonstrate that RBL2/p130 is a direct substrate of SCFcyclin F. We map a cyclin F regulatory site to a flexible linker in the p130 pocket domain, and show that this site mediates binding, stability, and ubiquitination. Expression of a non-degradable p130 represses cell cycle gene expression and strongly reduces proliferation. These data suggest that SCFcyclin F plays a key role in the CDK-RB network and raises the possibility that aberrant p130 degradation could dysregulate the cell cycle in human cancers.

cell biology↗

bioRxiv2021

The structure of the human cell cycle

The human cell cycle is conventionally depicted as a five-phase model consisting of four proliferative phases (G1, S, G2, M) and a single state of arrest (G0). However, recent studies show that individual cells can take different paths through the cell cycle and exit into distinct arrest states, thus necessitating an update to the canonical model. We combined time lapse microscopy, highly multiplexed single cell imaging and manifold learning to determine the underlying "structure" of the human cell cycle under multiple growth and arrest conditions. By visualizing the cell cycle as a complete biological process, we identified multiple points of divergence from the proliferative cell cycle into distinct states of arrest, revealing multiple mechanisms of cell cycle exit and re-entry and the molecular routes to senescence, endoreduplication and polyploidy. These findings enable the visualization and comparison of alternative cell cycles in development and disease. One-sentence summaryA systems-level view of single-cell states reveals the underlying architecture of the human cell cycle

cell biology↗

bioRxiv2020

Hyperactive end joining repair mediates resistance to DNA damaging therapy in p53-deficient cells

TP53 mutations in cancer are associated with poor patient outcomes and resistance to DNA damaging therapies1-3. However, the mechanisms underlying treatment resistance in p53-deficient cells remain poorly characterized. Here, we show that p53-deficient cells exhibit hyperactive repair of therapy-induced DNA double strand breaks (DSBs), which is suppressed by inhibition of DNA-dependent protein kinase (DNA-PK). Single-cell analyses of DSB repair kinetics and cell cycle state transitions reveal an essential role for DNA-PK in suppressing S phase DNA damage and mitotic catastrophe in p53-deficient cells. Yet, a subset of p53-deficient cells exhibit intrinsic resistance to therapeutic DSBs due to a repair pathway that is not sensitive to DNA-PK inhibition. We show that p53 deficiency induces overexpression of DNA Polymerase Theta (Pol {theta}), which mediates an alternative end-joining repair pathway that becomes hyperactivated by DNA-PK inhibition4. Combined inhibition of DNA-PK and Pol {theta} restores therapeutic DNA damage sensitivity in p53-deficient cells. Thus, our study identifies two targetable DSB end joining pathways that can be suppressed as a strategy to overcome resistance to DNA-damaging therapies in p53-deficient cancers.

cell biology↗

bioRxiv2019

Inferring the structures of signaling motifs from paired dynamic traces of single cells

Individual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained by a single signaling motif that deterministically maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in single cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cells upstream signal was randomly paired with another cells downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be embedded within the dynamical patterns of single cells.\n\nAuthor SummaryCells use molecular signaling networks to translate dynamically changing stimuli into appropriate downstream responses. Specialized network structures, or motifs, allow cells to properly decode a variety of temporal input signals. In this paper, we present an algorithm that infers signaling motifs from multiple examples of an upstream signal paired with its downstream response in the same cell. We compare the predictive power of single-cell versus averaged time-series traces and the incremental benefit of adding more single-cell traces to the algorithm. We use this approach to understand how yeast respond to environmental stresses.

systems biology↗

bioRxiv2019

Autophagy regulates the localization and degradation of p16INK4a

The tumor suppressor protein p16INK4a (p16) is a well-established hallmark of aging that induces cellular senescence in response to stress. Previous studies have focused primarily on p16 regulation at the transcriptional level; comparatively little is known about the proteins intracellular localization and degradation. The autophagy-lysosomal pathway has been implicated in the subcellular trafficking and turnover of various stress-response proteins, but it is unclear whether p16 is involved in these pathways. Here, we investigate the role of autophagy, vesicular trafficking, and lysosomal degradation on p16 expression and localization in human epithelial cells. Time-lapse fluorescence microscopy using an endogenous p16-mCherry reporter revealed that autophagy induced by genotoxic stress stimulates rapid p16 recruitment to acidic cytoplasmic vesicles. When vesicular acidification was inhibited by NH4Cl, nuclear p16 levels increased. Single-cell imaging revealed that p16 localizes to lysosomes upon stress, implicating the autophagy pathway as a regulator of p16 localization. Blocking autophagy with bafilomycin, chloroquine, or NH4Cl resulted in elevated p16 protein levels without increased transcription. Increased p16 coincided with accumulation of autophagosome chaperone p62/SQSTM1 (p62) and decreased levels of phosphorylated-Rb. Furthermore, chloroquine caused p16 aggregation within stalled vesicles containing autophagosome marker LC3, demonstrating that p16 is transported and degraded by the autophagy-lysosomal pathway. Knockdown of p62 resulted in delocalization of p16 aggregates to autophagosomes, suggesting that p16 is targeted to these vesicles by p62. Taken together, these results implicate the autophagy pathway as a novel regulator of p16 degradation and localization, which could play a role in the etiology of cancer and age-related diseases.

cell biology↗

bioRxiv2018

Precise detection of S phase onset reveals decoupled G1/S transition events

The eukaryotic cell division cycle is the process by which cells duplicate their genomes and proliferate. Transitions between sequential cell cycle phases are tightly orchestrated to ensure precise and efficient cell cycle progression. Interrogating molecular events at these transitions is important for understanding normal and pathological cell proliferation and mechanisms that ensure genome stability. A popular fluorescent reporter system known as \"FUCCI\" has been widely adopted for identifying cell cycle phases. Using time-lapse fluorescence microscopy, we quantitatively analyzed the dynamics of the FUCCI reporters relative to the transitions into and out of S phase. Although the original reporters reflect the E3 ubiquitin ligase activities for which they were designed, SCFSkp2 and APCCdh1, their dynamics are significantly and variably offset from actual S phase boundaries. To precisely mark these transitions, we generated and thoroughly validated a new reporter containing a PCNA-interacting protein degron whose oscillations are directly coupled to the process of DNA replication itself. We combined this reporter with the geminin-based APCCdh1 reporter to create \"PIP-FUCCI.\" PIP degron reporter dynamics closely correlate with S phase transitions irrespective of reporter expression levels. Using PIP-FUCCI, we made the unexpected observation that the apparent timing of APCCdh1 inactivation frequently varies relative to the onset of S phase. We demonstrate that APCCdh1 inactivation is not a strict pre-requisite for S phase entry, though delayed APCCdh1 inactivation correlates with longer S phase. Our results illustrate the benefits of precise delineation of cell cycle phase boundaries for uncovering the sequences of molecular events at critical cell cycle transitions.

cell biology↗