Biology subjects

Levitin, H. M.

Publications and source records attributed to Levitin, H. M..

6 recordsLinked to original sources

bioRxiv2025

Transcriptional control of T cell tissue adaptation and effector function in infants and adults

The first years of life are essential for the development of memory T cells, which rapidly populate the bodys diverse tissue sites during infancy. However, the degree to which tissue memory T cell responses in early life reflect those during adulthood is unclear. Here, we use single cell RNA-sequencing of resting and ex vivo activated T cells from lymphoid and mucosal tissues of infant (aged 2-9 months) and adult (aged 40-65 years) human organ donors to dissect the transcriptional programming of memory T cells over age. Infant memory T cells demonstrate a unique stem-like transcriptional profile and tissue adaptation program, yet exhibit reduced activation capacity and effector function relative to adults. Using CRISPR-Cas9 knockdown, we define Helios (IKZF2) as a critical transcriptional regulator of the infant-specific tissue adaptation program and restricted effector state. Our findings reveal key transcriptional mechanisms that control tissue T cell fate and function in early life.

immunology↗

bioRxiv2023

Consensus scHPF Identifies Cell Type-Specific Drug Responses in Glioma by Integrating Large-Scale scRNA-seq

Single-cell transcriptomic analyses now frequently involve elaborate study designs including samples from multiple individuals, experimental conditions, perturbations, and batches from complex tissues. Dimensionality reduction is required to facilitate integration, interpretation, and statistical analysis. However, these datasets often include subtly different cellular subpopulations or state transitions, which are poorly described by clustering. We previously reported a Bayesian matrix factorization algorithm called single-cell hierarchical Poisson factorization (scHPF) that identifies gene co-expression patterns directly from single-cell RNA-seq (scRNA-seq) count matrices while accounting for transcript drop-out and noise. Here, we describe consensus scHPF, which analyzes scHPF models from multiple random initializations to identify the most robust gene signatures and automatically determine the number of factors for a given dataset. Consensus scHPF facilitates integration of complex datasets with highly multi-modal posterior distributions, resulting in factors that can be uniformly analyzed across individuals and conditions. To demonstrate the utility of consensus scHPF, we performed a meta-analysis of a large-scale scRNA-seq dataset from drug-treated, human glioma slice cultures generated from surgical specimens across three major cell types, 19 patients, 10 drug treatment conditions, and 52 samples. In addition to recapitulating previously reported cell type-specific drug responses from smaller studies, consensus scHPF identified disparate effects of the topoisomerase poisons etoposide and topotecan that are highly consistent with the distinct roles and expression patterns of their respective protein targets.

genomics↗

bioRxiv2020

Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-seq

Precision oncology requires the timely selection of effective drugs for individual patients. An ideal platform would enable rapid screening of cell type-specific drug sensitivities directly in patient tumor tissue and reveal strategies to overcome intratumoral heterogeneity. Here we combine multiplexed drug perturbation in acute slice culture from freshly resected tumors with single-cell RNA sequencing (scRNA-seq) to profile transcriptome-wide drug responses. We applied this approach to glioblastoma (GBM) and demonstrated that acute slice cultures from individual patients recapitulate the cellular and molecular features of the originating tumor tissue. Detailed investigation of etoposide, a topoisomerase poison, and the histone deacetylase (HDAC) inhibitor panobinostat in acute slice cultures revealed cell type-specific responses across multiple patients, including unexpected effects on the immune microenvironment. We anticipate that this approach will facilitate rapid, personalized drug screening to identify effective therapies for solid tumors.

cancer biology↗

bioRxiv2019

A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease

Human T cells coordinate adaptive immunity by localization in diverse tissue sites, though blood T cells are the most readily studied. Here, we used single-cell RNA-seq to define the functional responses of T cells isolated from human lungs, lymph nodes, bone marrow, and blood to TCR-stimulation. We reveal how human T cells in tissues relate to those in blood, and define activation states for CD4+ and CD8+T cells across all sites, including an interferon-response state for CD4+T cells and distinct effector states for CD8+T cells. We further show how profiles of individual tumor-associated T cells can be projected onto this healthy reference map, revealing their functional state.

immunology↗

bioRxiv2018

De novo Gene Signature Identification from Single-Cell RNA-Seq with Hierarchical Poisson Factorization

Common approaches to gene signature discovery in single cell RNA-sequencing (scRNA-seq) depend upon predefined structures like clusters or pseudo-temporal order, require prior normalization, or do not account for the sparsity of single cell data. We present single cell Hierarchical Poisson Factorization (scHPF), a Bayesian factorization method that adapts Hierarchical Poisson Factorization [1] for de novo discovery of both continuous and discrete expression patterns from scRNA-seq. scHPF does not require prior normalization and captures statistical properties of single cell data better than other methods in benchmark datasets. Applied to scRNA-seq of the core and margin of a high-grade glioma, scHPF uncovers marked differences in the abundance of glioma subpopulations across tumor regions and subtle, regionally-associated expression biases within glioma subpopulations. scHFP revealed an expression signature that was spatially biased towards the glioma-infiltrated margins and associated with inferior survival in glioblastoma.

systems biology↗

bioRxiv2018

Single-Cell Transcriptome Analysis of Lineage Diversity and Microenvironment in High-Grade Glioma

BackgroundDespite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs). We sampled the cellular milieu of HGGs with massively-parallel single-cell RNA-Seq.\n\nResultsWhile HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, reminiscent of molecular interactions between glioma and immune cells established in animal models. Additionally, we discovered a tight coupling between lineage resemblance and proliferation among malignantly transformed cells. Glioma cells that resemble oligodendrocyte progenitors, which proliferate in the brain, are often found in the cell cycle. Conversely, glioma cells that resemble astrocytes, neuroblasts, and oligodendrocytes, which are non-proliferative in the brain, are generally non-cycling in tumors.\n\nConclusionsThese studies reveal a relationship between cellular identity and proliferation in HGG and distinct population structures that reflects the extent of neural and non-neural lineage resemblance among malignantly transformed cells.

cancer biology↗