Biology subjects

Weiner, A. C.

Publications and source records attributed to Weiner, A. C..

9 recordsLinked to original sources

bioRxiv2026

Using image classifiers to predict CMT2A disease-relevant mitochondrial motility phenotypes in iPSC motor neurons

Charcot-Marie-Tooth disease type 2A (CMT2A) is a genetic disease characterized by autosomal dominant MFN2 mutations and dysregulated mitochondrial trafficking. While there is currently no FDA-approved CMT2A therapy, the recent development of iPSC motor neuron model systems, high-throughput imaging platforms, and CRISPR-based gene editing technologies holds promise for screening new therapies at scale in vitro. A critical roadblock for therapeutic screening is the development of scalable and robust computational methods to assess the mitochondrial trafficking phenotypes, healthy or diseased, of each iPSC motor neuron sample. To address this gap, we developed a vision transformer (ViT) based classification framework that predicts disease phenotypes using kymographs, an image transformation that captures particle movement along a prespecified path, such as mitochondrial movement along axons. We show that our classification approach more accurately discriminates healthy MFN2 wild-type (WT) from diseased MFN2 R364W-mutant (R364W) iPSCs than alternative summary statistics, such as mitochondrial speed and fraction of stationary mitochondria that are directly extracted from kymographs. Furthermore, we show that our model maintains high accuracy when deployed on a biological replicate holdout dataset. An analysis of ViT patch embeddings of the kymographs shows that mitochondria with highly variable sizes and many intersection events most strongly associate with R364W diseased kymographs. The computational approach demonstrated in this paper has broad applicability for future high-throughput screens where organelle trafficking along axons plays a key role in disease pathogenesis.

cell biology↗

bioRxiv2025

Live-cell analyses with unsegmented images to study cancer cell response to modified T cell therapy

Live-cell imaging (LCI) of modified T cells co-cultured with cancer cells is commonly used to quantify T cell anti-cancer function. Videos captured by LCI show complex multi-cell behavioral phenotypes that go beyond simple cancer cell fluorescence measurements. Here, we develop an unsupervised analysis workflow to characterize LCI data generated using the Incucyte imaging platform. Unlike most LCI analyses, we avoid cell segmentation due to the low spatiotemporal resolution of the LCI videos and high levels of cell-cell contact. Instead, we develop methods that identify global aggregation patterns and local cellular keypoints to characterize the multicellular interactions that determine cancer cell sensitivity to, or escape from, T cell surveillance. We demonstrate our segmentation-free live-cell behavioral analysis (SF-LCBA) methods on TCR T cells from four donors with varying proportions of cells with a beneficial RASA2 knockout and effector-to-target initial concentrations in a co-culture with A375 melanoma cells. We find that different T cell modifications affect the spatiotemporal dynamics of multicellular aggregate formation. In particular, we show that fewer and smaller cancer cell aggregates form at high ratios of effector T cells to target cancer cells and high titrations of T cells with RASA2 knockouts. Our SF-LCBA method identifies, characterizes, and tracks cellular aggregate formation in datasets that are unsuitable for cell segmentation and tracking, opening the door to more therapeutically-relevant measurements of modified T cell therapy cell behavioral phenotypes from LCI data.

cancer biology↗

bioRxiv2024

Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime, we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily active mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.

cancer biology↗

bioRxiv2024

Luminal breast epithelial cells from wildtype and BRCA mutation carriers harbor copy number alterations commonly associated with breast cancer

Cancer-associated mutations have been documented in normal tissues, but the prevalence and nature of somatic copy number alterations and their role in tumor initiation and evolution is not well understood. Here, using single cell DNA sequencing, we describe the landscape of CNAs in >42,000 breast epithelial cells from women with normal or high risk of developing breast cancer. Accumulation of individual cells with one or two of a specific subset of CNAs (e.g. 1q gain and 16q, 22q, 7q, and 10q loss) is detectable in almost all breast tissues and, in those from BRCA1 or BRCA2 mutations carriers, occurs prior to loss of heterozygosity (LOH) of the wildtype alleles. These CNAs, which are among the most common associated with ductal carcinoma in situ (DCIS) and malignant breast tumors, are enriched almost exclusively in luminal cells not basal myoepithelial cells. Allele-specific analysis of the enriched CNAs reveals that each allele was independently altered, demonstrating convergent evolution of these CNAs in an individual breast. Tissues from BRCA1 or BRCA2 mutation carriers contain a small percentage of cells with extreme aneuploidy, featuring loss of TP53, LOH of BRCA1 or BRCA2, and multiple breast cancer-associated CNAs in addition to one or more of the common CNAs in 1q, 10q or 16q. Notably, cells with intermediate levels of CNAs are not detected, arguing against a stepwise gradual accumulation of CNAs. Overall, our findings demonstrate that chromosomal alterations in normal breast epithelium partially mirror those of established cancer genomes and are chromosome- and cell lineage-specific.

cancer biology↗

bioRxiv2023

Modeling single cell DNA replication dynamics and aneuploidy in genomically unstable cancers

Dysregulated DNA replication is both a cause and a consequence of aneuploidy, yet the dynamics of DNA replication in aneuploid cell populations remains understudied. We developed a new method, PERT, for inferring cell-specific DNA replication states from single-cell whole genome sequencing, and investigated clone-specific DNA replication dynamics in >50,000 cells obtained from a collection of aneuploid and clonally heterogeneous cell lines, xenografts and primary cancer tissues. Clone replication timing (RT) profiles correlated with future copy number changes in serially passaged cell lines. Cell type was the strongest determinant of RT heterogeneity, while whole genome doubling and mutational process were associated with accumulation of late S-phase cells and weaker RT associations. Copy number changes affecting chromosome X had striking impact on RT, with loss of the inactive X allele shifting replication earlier, and loss of inactive Xq resulting in reactivation of Xp. Finally, analysis of time series xenografts illustrate how cell cycle distributions approximate clone proliferation, recapitulating expected relationships between proliferation and fitness in treatment-naive and chemotherapeutic contexts.

cancer biology↗

bioRxiv2023

Exploiting allele-specific transcriptional effects of subclonal copy number alterations for genotype-phenotype mapping in cancer cell populations

Somatic copy number alterations drive aberrant gene expression in cancer cells. In tumors with high levels of chromosomal instability, subclonal copy number alterations (CNAs) are a prevalent feature which often result in heterogeneous cancer cell populations with distinct phenotypes1. However, the extent to which subclonal CNAs contribute to clone-specific phenotypes remains poorly understood, in part due to the lack of methods to quantify how CNAs influence gene expression at a subclone level. We developed TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population and explicitly models gene dosage effects from subclonal alterations. We show through quantitative benchmarking data and application to human cancer data with single cell DNA and RNA libraries that TreeAlign accurately encodes clone-specific transcriptional effects of subclonal CNAs, the impact of allelic imbalance on allele-specific transcription, and obviates the need to arbitrarily define genotypic clones from a phylogenetic tree a priori. Combined, these advances lead to highly granular definitions of clones with distinct copy-number driven expression programs with increased resolution and accuracy over competing methods. The resulting improvement in assignment of transcriptional phenotypes to genomic clones enables clone-clone gene expression comparisons and explicit inference of genes that are mechanistically altered through CNAs, and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer.

cancer biology↗

bioRxiv2021

Evolutionary tracking of cancer haplotypes at single-cell resolution

Cancer genomes exhibit extensive chromosomal copy number changes and structural variation, yet how allele specific alterations drive cancer genome evolution remains unclear. Here, through application of a new computational approach we report allele specific copy number alterations in 11,097 single cell whole genomes from genetically engineered mammary epithelial cells and 21,852 cells from high grade serous ovarian and triple negative breast cancers. Resolving single cell copy number profiles to individual alleles uncovered genomic background distributions of gains, losses and loss of heterozygosity, yielding evidence of positive selection of specific chromosomal alterations. In addition specific genomic loci in maternal and paternal alleles were commonly found to be altered in parallel with convergent phenotypic transcriptional effects. Finally we show that haplotype specific alterations trace the cyclical etiology of high level amplifications and reveal clonal haplotype decomposition of complex structures. Together, our results illuminate how allele and haplotype specific alterations, here determined across thousands of single cell cancer genomes, impact the etiology and evolution of structural variations in human tumours.

cancer biology↗

bioRxiv2021

The impact of mutational processes on structural genomic plasticity in cancer cells

Structural genome alterations are determinants of cancer ontogeny and therapeutic response. While bulk genome sequencing has enabled delineation of structural variation (SV) mutational processes which generate patterns of DNA damage, we have little understanding of how these processes lead to cell-to-cell variations which underlie selection and rates of accrual of different genomic lesions. We analysed 309 high grade serous ovarian and triple negative breast cancer genomes to determine their mutational processes, selecting 22 from which we sequenced >22,000 single cell whole genomes across a spectrum of mutational processes. We show that distinct patterns of cell-to-cell variation in aneuploidy, copy number alteration (CNA) and segment length occur in homologous recombination deficiency (HRD) and fold-back inversion (FBI) phenotypes. Widespread aneuploidy through induction of HRD through BRCA1 and BRCA2 inactivation was mirrored by continuous whole genome duplication in HRD tumours, contrasted with early ploidy fixation in FBI. FBI tumours exhibited copy number distributions skewed towards gains, widespread clone-specific variation in amplitude of high-level amplifications, often impacting oncogenes, and break-point variability consistent with progressive genomic diversification, which we termed serriform structural variation (SSV). SSVs were consistent with a CNA-based molecular clock reflecting a continual and distributed process across clones within tumours. These observations reveal previously obscured genome plasticity and evolutionary properties with implications for cancer evolution, therapeutic targeting and response.

cancer biology↗

bioRxiv2019

Modeling Cell-Specific Dynamics and Regulation of the Common Gamma Chain Cytokines

Many receptor families exhibit both pleiotropy and redundancy in their regulation, with multiple ligands, receptors, and responding cell populations. Any intervention, therefore, has multiple effects, confounding intuition about how to precisely manipulate signaling for therapeutic purposes. The common {gamma}-chain cytokine receptor dimerizes with complexes of the cytokines interleukin (IL)-2, IL-4, IL-7, IL-9, IL-15, and IL-21 and their corresponding "private" receptors. These cytokines have existing uses and future potential as immune therapies due to their ability to regulate the abundance and function of specific immune cell populations. However, engineering cell specificity into a therapy is confounded by the complexity of the family across responsive cell types. Here, we build a binding-reaction model for the ligand-receptor interactions of common {gamma}-chain cytokines enabling quantitative predictions of response. We show that accounting for receptor-ligand trafficking is essential to accurately model cell response. This model accurately predicts ligand response across a wide panel of cell types under diverse experimental designs. Further, we can predict the effect and specificity of natural or engineered ligands across cell types. We then show that tensor factorization is a uniquely powerful tool to visualize changes in the input-output behavior of the family across time, cell types, ligands, and concentration. In total, these results present a more accurate model of ligand response validated across a panel of immune cell types, and demonstrate an approach for generating interpretable guidelines to manipulate the cell type-specific targeting of engineered ligands. These techniques will in turn help to study and therapeutically manipulate many other complex receptor-ligand families. Summary pointsO_LIA dynamical model of the {gamma}-chain cytokines accurately models responses to IL-2, IL-15, IL-4, and IL-7. C_LIO_LIReceptor trafficking is necessary for capturing ligand response. C_LIO_LITensor factorization maps responses across cell populations, receptors, cytokines, and dynamics to visualize cytokine specificity. C_LIO_LIAn activation model coupled with tensor factorization provides design specifications for engineering cell-specific responses. C_LI

immunology↗