Hanna Mendes Levitin’s research while affiliated with Columbia University and other places

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Publications (19)


FIGURES 796
Transcriptional control of T cell tissue adaptation and effector function in infants and adults
  • Preprint
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February 2025

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24 Reads

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Hanna M Levitin

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Thomas J Connors

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Peter A Sims

The first years of life are essential for the development of memory T cells, which rapidly populate the body's 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.

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Figure 4. Fold-changes in the frequencies of cells with high cell scores in drug-treated vs. vehicle control-treated slice cultures in the transformed glioma cell scHPF model for A) a proliferation factor; B) a metallothionein factor; C) an astrocyte/mesenchymal factor; and D) a mesenchymal factor. Here, each dot represents an individual patient (i.e. biological replicates). For each drug, ** indicates FDR<0.05 based on a linear mixed model.
Consensus scHPF Identifies Cell Type-Specific Drug Responses in Glioma by Integrating Large-Scale scRNA-seq

December 2023

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31 Reads

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1 Citation

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.


a Schematic illustration of experimental and analytical methods for slice culture drug perturbation and scRNA-seq. b UMAP embedding of scRNA-seq profiles from acutely isolated biopsies and slice cultures from different regions of the same tumor (PW032) colored by sample origin. c Same as b but colored by the log-ratio of Chr. 7 to Chr. 10 average expression where a high ratio (red) indicates malignant transformation. d Same as b but colored by cell type. e Heatmap of average expression of marker genes from cell types in the tumor microenvironment in each cell type and sample from PW032. f Fractional abundance of each major cell type in each biopsy and slice culture sample from PW032. g Two-dimensional model projecting each transformed cell from PW032 biopsies and slice into four major GBM transformed populations colored by sample origin
a Fractional abundance of each major cell type in all untreated slice culture scRNA-seq data sets from the six patients in the study. b Two-dimensional model projecting each transformed cell from all untreated slice culture scRNA-seq data sets from the six patients in the study. c UMAP embedding of scRNA-seq profiles from five untreated slice cultures taken within 3.5 mm of each other from PW040 colored by sample of origin. d Same as b but for the transformed cells from the five untreated slice cultures from PW040
a Experimental schematic for slice culture drug screening (6 drugs, 2 controls) from a single patient (PW030). b Heatmap showing the number of differentially expressed genes (FDR<0.01) in the tumor, myeloid, and oligodendrocyte populations between treated and control slices for each drug in the screen illustrated in a. c Same as b but showing only differentially expressed genes with FDR<0.01 and fold-change amplitude greater than two (both up- and downregulated genes). d UMAP embedding of scRNA-seq profiles of transformed cells from the control slices colored by expression of two proliferation markers (TOP2A, MKI67), two mesenchymal markers (CD44, VIM), and an astrocyte marker (GFAP). e Same as d but with UMAP projection density of scRNA-seq profiles of transformed cell from the treated slice cultures for each drug. Note that there is negligible projection density for the etoposide-treated cells onto the control cells for the small proliferative population expressing TOP2A and MKI67
a UMAP embedding of scRNA-seq profiles from slice cultures of six patients generated using the cell score matrix from joint scHPF analysis of the entire data set colored by patient. b Same as a but colored by treatment condition. c Same as a but colored by the scHPF-imputed log-ratio of Chr. 7 to Chr. 10 average expression where a high ratio (red) indicates malignant transformation. d Same as a but colored by expression of the oligodendrocyte marker PLP1. e Same as a but colored by expression of the myeloid marker CD14. f Same as a but colored by the total expression of the T cell receptor constant regions (TRAC, TRBC1, TRBC2). g Heatmap showing the log-ratio of the average expression of the top 100 genes in each eptoposide-treated to each control slice for each scHPF factor and each of three cell types—transformed (tumor), oligodendrocyte (oligo), and myeloid. h Same as g for panobinostat-treated slices. i Violin plots showing the distributions of the average expression of the top 100 genes in the Proliferation scHPF factor for each vehicle- and etoposide-treated slice for each patient in tumor cells. All within-patient, vehicle-treatment comparisons have p<0.05 (Mann-Whitney U-test) unless otherwise indicated (N.S. or not significant). j Same as i for the Panobinostat1/MT scHPF factor for each vehicle- and panobinostat-treated slice in tumor cells. k Same as j for the Panobinostat2/Chemokine scHPF factor in tumor cells. l Same as j for the Panobinostat3/Oligo scHPF factor in oligodendrocytes. m Same as j for the Myeloid2/Pro-Inflammatory scHPF factor in myeloid cells. n Same as j for the Myeloid3/CD163 scHPF factor in myeloid cells
Ten slices from a single patient (TB6393) were treated with panobinostat (3 slices), etoposide (3 slices), or vehicle (DMSO, 4 slices adjacent to drug-treated slices). a UMAP embedding of scRNA-seq profiles of ten slices colored by treatment condition. b Same as a but colored by the log-ratio of Chr. 7 to Chr. 10 average expression where a high ratio (red) indicates malignant transformation. c Same as b but colored by cell type. d Volcano plot of differential expression analysis between all transformed tumor cells from etoposide-treated slices and all adjacent vehicle-treated slices. Genes highlighted in red and blue have fold-increase or decrease, respectively, greater than two and FDR<0.05. A large set of cell cycle control markers highly downregulated in the etoposide-treated cells are labeled and highlighted in cyan. e Same as d but for panobinostat-treated transformed tumor cells showing strong induction of metallothioneins and several mature neuronal markers labeled and highlighted in orange. f Same as e but for the myeloid cells showing downregulation of the macrophage markers highlighted in cyan and strong induction of metallothioneins highlighted in orange. g Heatmap showing the normalized enrichment score (NES) from gene set enrichment analysis (GSEA) analysis. GSEA was performed using gene sets from the top 100 genes of each scHPF factor from Fig. 4 to analyze the ranked differentially expression genes between tumor or myeloid cells from each etoposide-treated slice and that of its adjacent vehicle-treated slice. scHPF factor with consistent enrichment and FDR<0.05 in at least 2 treated vs. untreated comparisons are marked with asterisk. h Same as g but showing NES from GSEA analysis for differentially expression genes between tumor or myeloid cells from each panobinostat-treated slice and that of its adjacent vehicle-treated slice
Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq

May 2021

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300 Reads

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67 Citations

Genome Medicine

Background Preclinical studies require models that recapitulate the cellular diversity of human tumors and provide insight into the drug sensitivities of specific cellular populations. The ideal platform would enable rapid screening of cell type-specific drug sensitivities directly in patient tumor tissue and reveal strategies to overcome intratumoral heterogeneity. Methods 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 in individual patients. We applied this approach to drug perturbations on slices derived from six glioblastoma (GBM) resections to identify conserved drug responses and to one additional GBM resection to identify patient-specific responses. Results We used scRNA-seq to demonstrate that acute slice cultures recapitulate the cellular and molecular features of the originating tumor tissue and the feasibility of drug screening from an individual tumor. 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. Etoposide has a conserved impact on proliferating tumor cells, while panobinostat treatment affects both tumor and non-tumor populations, including unexpected effects on the immune microenvironment. Conclusions Acute slice cultures recapitulate the major cellular and molecular features of GBM at the single-cell level. In combination with scRNA-seq, this approach enables cell type-specific analysis of sensitivity to multiple drugs in individual tumors. We anticipate that this approach will facilitate pre-clinical studies that identify effective therapies for solid tumors.


Figure 2. A) Experimental schematic for slice culture drug screening (6 drugs, 2 controls) from a single patient (PW030). B) Heatmap showing the number of differentially expressed genes (FDR<0.01) in the tumor, myeloid, and oligodendrocyte populations between treated and control slices for each drug in the screen illustrated in A). C) Same as B) but showing only differentially expressed genes with FDR<0.01 and fold-change amplitude greater than two (both up-and down-regulated genes). D) UMAP embedding of scRNA-seq profiles of transformed cells from the control slices colored by expression of two proliferation markers (TOP2A, MKI67), two mesenchymal markers (CD44, VIM), and an astrocyte marker (GFAP). E) Same as D) but with UMAP projection density of scRNA-seq profiles of transformed cell from the treated slice cultures for each drug. Note that there is negligible projection density for the etoposide-treated cells onto the control cells for the small proliferative population expressing TOP2A and MKI67.
Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-seq

April 2020

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208 Reads

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3 Citations

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.


Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease

October 2019

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2,619 Reads

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538 Citations

Human T cells coordinate adaptive immunity in diverse anatomic compartments through production of cytokines and effector molecules, but it is unclear how tissue site influences T cell persistence and function. Here, we use single cell RNA-sequencing (scRNA-seq) to define the heterogeneity of human T cells isolated from lungs, lymph nodes, bone marrow and blood, and their functional responses following stimulation. Through analysis of >50,000 resting and activated T cells, we reveal tissue T cell signatures in mucosal and lymphoid sites, and lineage-specific activation states across all sites including distinct effector states for CD8⁺ T cells and an interferon-response state for CD4⁺ T cells. Comparing scRNA-seq profiles of tumor-associated T cells to our dataset reveals predominant activated CD8⁺ compared to CD4⁺ T cell states within multiple tumor types. Our results therefore establish a high dimensional reference map of human T cell activation in health for analyzing T cells in disease.


Single cell transcriptomics resolves activation dynamics and cellular states of human blood and tissue T cells

May 2019

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2 Reads

The Journal of Immunology

T cells persist as heterogeneous subsets throughout the body and are essential in mounting protective immune responses. In healthy humans, most of our knowledge of T cell activation derives from sampling the peripheral blood and therefore the transcriptional states of tissue T cells, their functional responses to stimulation, and how they relate to T cells in blood have been poorly defined. Here, we profile the activation dynamics of T cells isolated from human lungs (LG), lymph nodes (LN), bone marrow (BM) and blood following TCR-stimulation by single cell RNA-sequencing (scRNA-seq). Analysis of >50,000 individual resting and activated T cells using clustering and new factorization methods reveals lineage-specific gene expression signatures and discrete activation trajectories in all tissues. Between sites, T cells from LG and LN are most distinct, while blood T cells are most similar to those in BM but persist in a more activated basal state. We identify a common transcriptional profile of tissue T cells and detect trace numbers of these cells in the blood. We also define cellular states for resting and activated T cells across tissues, including an interferon-induced state in CD4+ T cells and distinct effector states specific to CD8+ T cells, and uncover new markers of T cell activation that may be central to T cell function. Importantly, we demonstrate that the T cell activation states resolved here serve as a new baseline for defining T cell dysfunction in disease, revealing novel insights into T cell states among tumor-infiltrating lymphocytes from previous studies. Our investigation couples scRNA-seq with new analysis methods to define the activation dynamics of human T cells from disparate anatomical sites on a single cell level.






Citations (10)


... In order to assess whether the tissue imparted distinct gene signatures across lineages, we implemented consensus single cell hierarchical Poisson Factorization (scHPF) 11,35 , a Bayesian factorization algorithm that identifies the major co-expression patterns ("factors") in the data, rather than considering each gene in isolation (as we have done for DE). We applied scHPF to each major immune cell lineage in separate models with balanced cell input across tissues ( Supplementary Table 7 ), which allowed us to identify a variety of transcriptional programs. ...

Reference:

Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age
Consensus scHPF Identifies Cell Type-Specific Drug Responses in Glioma by Integrating Large-Scale scRNA-seq

... Furthermore, these transcriptional states seem to be highly plastic 25,26 , which may result in a complicated relationship between somatic mutations and transcriptional states. In addition, recent studies in acute slice cultures 27 have uncovered cell-type-specific drug responses in GBM, but have relied on transcriptional profiling to define drug-sensitive cellular subpopulations, which could be further refined by their genetics. Thus, joint single-nucleus RNA/DNA sequencing could be a compelling approach to analyzing these tumors and their response to therapy. ...

Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq

Genome Medicine

... Factor decomposition methods break down expression values into different components of variation. Matrix factorization has proven popular, both in bulk (Squires et al., 2020;Zhao et al., 2020) and single cell (Mohammadi et al., 2020) genomics analyses, for its interpretability and scalability with larger datasets (Stein-O'Brien et al., 2018); for example, decomposing expression values in scRNA can cluster and rank genes into groups which are readily interpretable as mechanisms, pathways, or processes, most often through GO enrichment. Using this concept of factor decomposition, MUSIC (Duan et al., 2019) groups genes as ''topics'' through topic modeling and is able to quantify the size of the perturbational effect using the differential activation of all topics. ...

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

... Sixteen (16,50.0%) patients were infected with HPV-6 only, ten (10,31.3%) were infected with HPV-11 only, and two (2, 6.3%) were coinfected with both HPV-6 and HPV-11. Four (4, 12.5%) patients' genotypes could not be distinguished between HPV-6 and HPV-11 per assay standards at Screening but were known from prior diagnostic history to be HPV-6 and/or HPV-11 positive, which was subsequently confirmed through on-study assessment. ...

Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease

... For example, depending on their location in either the dorsal or ventral SVZ, NPCs can generate different OB interneurons and/or oligodendroglial cells (Bond & Song, 2021;Delgado et al., 2021;Merkle et al., 2014). Single-cell RNA-sequencing of the SVZ niche further confirms this regional heterogeneity in a spatially restricted manner (Llorens-Bobadilla et al., 2015;Mizrak et al., 2019). ...

Single-Cell Analysis of Regional Differences in Adult V-SVZ Neural Stem Cell Lineages

Cell Reports

... For the human GBM sample, we analyzed nuclei with >1,000 unique transcripts and >100,000 unique DNA fragments detected. We identified highly variable genes from the snRNA-seq count matrices based on their deviation from the gene drop-out curve as described by Levitin et al. 56 and used them to construct a Spearman's correlation matrix and k-nearest neighbor graph from which we performed unsupervised clustering using Louvain community detection as implemented in Phenograph 57 and visualized with UMAP 58 . The cluster-enriched genes that appear in gene expression heat maps (Figs. 2 and 4) are selected to highlight certain biological features of each cluster, but are all statistically enriched in a cluster with FDR < 0.05 based on the binomial test as described in Shekhar et al. 59 . ...

De novo gene signature identification from single‐cell RNA‐seq with hierarchical Poisson factorization

Molecular Systems Biology

... In this study, we analyzed publicly available scRNA-seq data from CD4-T, CD8-T and Treg cells isolated from melanoma [22,23], breast [24,25], lung [26,27], colorectal [28] and head and neck [29] cancer. We identified common genes among tumor-infiltrating T-cell subsets. ...

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

... Examples include mutations in IDH1 and amplification of PDGFRA with the proneural phenotype and NF1 mutations with the mesenchymal subtype 23 . scRNA-seq has shown that transformed glioma cells can take on any of multiple phenotypic states with varying degrees of neural lineage resemblance and that these states recur across patients 24 . Furthermore, these transcriptional states seem to be highly plastic 25,26 , which may result in a complicated relationship between somatic mutations and transcriptional states. ...

Single-cell transcriptome analysis of lineage diversity in high-grade glioma

Genome Medicine

... By examining these differentially expressed genes or specific cell subpopulations, notable distinctions in expression patterns between the two groups can be identified. Such differences hold promise as potential biomarkers, offering valuable insights into the mechanisms of drug resistance and paving the way for more personalized treatment approaches [31,[42][43][44][45][46]. ...

Single-Cell Transcriptomic Analysis of Tumor Heterogeneity
  • Citing Article
  • March 2018

Trends in Cancer