TAMI-74. SPATIOTEMPORAL MULTI-OMIC LANDSCAPE OF HUMAN MEDULLOBLASTOMA AT SINGLE CELL RESOLUTION

Abstract Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Here we use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Collectively, our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.

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Cross-species integration of single-cell RNA-seq resolved alveolar-epithelial transitional states in idiopathic pulmonary fibrosis

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00594.2020 ◽

2021 ◽

Author(s):

Kevin Y. Huang ◽

Enrico Petretto

Keyword(s):

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Single Cell ◽

Transitional Cell ◽

Notch Pathway ◽

Alveolar Epithelium ◽

State Transitions ◽

Specific Cell ◽

Differentiation Process ◽

Cell State

Single-cell transcriptomics analyses of the fibrotic lung uncovered two cell states critical to lung injury recovery in the alveolar epithelium- a reparative transitional cell state in the mouse and a disease-specific cell state (KRT5-/KRT17+) in human idiopathic pulmonary fibrosis (IPF). The murine transitional cell state lies between the differentiation from type 2 (AT2) to type 1 pneumocyte (AT1), and the human KRT5-/KRT17+ cell state may arise from the dysregulation of this differentiation process. We review major findings of single-cell transcriptomics analyses of the fibrotic lung and re-analyzed data from 7 single-cell RNA sequencing studies of human and murine models of IPF, focusing on the alveolar epithelium. Our comparative and cross-species single-cell transcriptomics analyses allowed us to further delineate the differentiation trajectories from AT2 to AT1 and AT2 to the KRT5-/KRT17+ cell state. We observed AT1 cells in human IPF retain the transcriptional signature of the murine transitional cell state. Using pseudotime analysis, we recapitulated the differentiation trajectories from AT2 to AT1 and from AT2 to KRT5-/KRT17+ cell state in multiple human IPF studies. We further delineated transcriptional programs underlying cell state transitions and determined the molecular phenotypes at terminal differentiation. We hypothesize that in addition to the reactivation of developmental programs (SOX4, SOX9), senescence (TP63, SOX4) and the Notch pathway (HES1) are predicted to steer intermediate progenitors to the KRT5-/KRT17+ cell state. Our analyses suggest that activation of SMAD3 later in the differentiation process may explain the fibrotic molecular phenotype typical of KRT5-/KRT17+ cells.

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Adult tissue-resident stem cells—fact or fiction?

Stem Cell Research & Therapy ◽

10.1186/s13287-021-02142-x ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Deepa Bhartiya

Keyword(s):

Stem Cells ◽

Single Cell ◽

Adult Stem Cells ◽

Cell Types ◽

Specific Cell ◽

Tissue Specific ◽

Cardiac Tissues ◽

Adult Tissues ◽

Resident Stem Cells ◽

Abundant Cytoplasm

AbstractLife-long tissue homeostasis of adult tissues is supposedly maintained by the resident stem cells. These stem cells are quiescent in nature and rarely divide to self-renew and give rise to tissue-specific “progenitors” (lineage-restricted and tissue-committed) which divide rapidly and differentiate into tissue-specific cell types. However, it has proved difficult to isolate these quiescent stem cells as a physical entity. Recent single-cell RNAseq studies on several adult tissues including ovary, prostate, and cardiac tissues have not been able to detect stem cells. Thus, it has been postulated that adult cells dedifferentiate to stem-like state to ensure regeneration and can be defined as cells capable to replace lost cells through mitosis. This idea challenges basic paradigm of development biology regarding plasticity that a cell enters point of no return once it initiates differentiation. The underlying reason for this dilemma is that we are putting stem cells and somatic cells together while processing for various studies. Stem cells and adult mature cell types are distinct entities; stem cells are quiescent, small in size, and with minimal organelles whereas the mature cells are metabolically active and have multiple organelles lying in abundant cytoplasm. As a result, they do not pellet down together when centrifuged at 100–350g. At this speed, mature cells get collected but stem cells remain buoyant and can be pelleted by centrifuging at 1000g. Thus, inability to detect stem cells in recently published single-cell RNAseq studies is because the stem cells were unknowingly discarded while processing and were never subjected to RNAseq. This needs to be kept in mind before proposing to redefine adult stem cells.

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A practical solution to pseudoreplication bias in single-cell studies

Nature Communications ◽

10.1038/s41467-021-21038-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Kip D. Zimmerman ◽

Mark A. Espeland ◽

Carl D. Langefeld

Keyword(s):

Single Cell ◽

Mixed Models ◽

Random Effect ◽

Cell Types ◽

Error Rates ◽

Specific Cell ◽

Experimental Conditions ◽

Type 1 Error ◽

Sample Correlation ◽

Inflated Type

AbstractCells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility. This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation. Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models. To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual. Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies.

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4 Molecularly guided multiplexed digital spatial analysis reveals differential gene expression profiles in the WNT-β-catenin pathway between melanoma and prostate tumors

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0004 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A4-A4

Author(s):

Anushka Dikshit ◽

Dan Zollinger ◽

Karen Nguyen ◽

Jill McKay-Fleisch ◽

Kit Fuhrman ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Molecule ◽

Spatial Information ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Fluorescence Assay ◽

Differentially Expressed ◽

Tumor Type ◽

Prostate Tumors

BackgroundThe canonical WNT-β-catenin signaling pathway is vital for development and tissue homeostasis but becomes strongly tumorigenic when dysregulated. and alter the transcriptional signature of a cell to promote malignant transformation. However, thorough characterization of these transcriptomic signatures has been challenging because traditional methods lack either spatial information, multiplexing, or sensitivity/specificity. To overcome these challenges, we developed a novel workflow combining the single molecule and single cell visualization capabilities of the RNAscope in situ hybridization (ISH) assay with the highly multiplexed spatial profiling capabilities of the GeoMx™ Digital Spatial Profiler (DSP) RNA assays. Using these methods, we sought to spatially profile and compare gene expression signatures of tumor niches with high and low CTNNB1 expression.MethodsAfter screening 120 tumor cores from multiple tumors for CTNNB1 expression by the RNAscope assay, we identified melanoma as the tumor type with the highest CTNNB1 expression while prostate tumors had the lowest expression. Using the RNAscope Multiplex Fluorescence assay we selected regions of high CTNNB1 expression within 3 melanoma tumors as well as regions with low CTNNB1 expression within 3 prostate tumors. These selected regions of interest (ROIs) were then transcriptionally profiled using the GeoMx DSP RNA assay for a set of 78 genes relevant in immuno-oncology. Target genes that were differentially expressed were further visualized and spatially assessed using the RNAscope Multiplex Fluorescence assay to confirm GeoMx DSP data with single cell resolution.ResultsThe GeoMx DSP analysis comparing the melanoma and prostate tumors revealed that they had significantly different gene expression profiles and many of these genes showed concordance with CTNNB1 expression. Furthermore, immunoregulatory targets such as ICOSLG, CTLA4, PDCD1 and ARG1, also demonstrated significant correlation with CTNNB1 expression. On validating selected targets using the RNAscope assay, we could distinctly visualize that they were not only highly expressed in melanoma compared to the prostate tumor, but their expression levels changed proportionally to that of CTNNB1 within the same tumors suggesting that these differentially expressed genes may be regulated by the WNT-β-catenin pathway.ConclusionsIn summary, by combining the RNAscope ISH assay and the GeoMx DSP RNA assay into one joint workflow we transcriptionally profiled regions of high and low CTNNB1 expression within melanoma and prostate tumors and identified genes potentially regulated by the WNT- β-catenin pathway. This novel workflow can be fully automated and is well suited for interrogating the tumor and stroma and their interactions.GeoMx Assays are for RESEARCH ONLY, not for diagnostics.

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Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types

Nature Metabolism ◽

10.1038/s42255-019-0152-6 ◽

2019 ◽

Vol 2 (1) ◽

pp. 97-109 ◽

Cited By ~ 17

Author(s):

Jinchu Vijay ◽

Marie-Frédérique Gauthier ◽

Rebecca L. Biswell ◽

Daniel A. Louiselle ◽

Jeffrey J. Johnston ◽

...

Keyword(s):

Adipose Tissue ◽

Single Cell ◽

Single Cell Analysis ◽

Human Adipose Tissue ◽

Cell Types ◽

Specific Cell ◽

Cell Analysis ◽

Disease Specific

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Identification and Characterization of Unique Subgroups of Chronic Pain Individuals with Dispositional Personality Traits

Pain Research and Management ◽

10.1155/2016/5187631 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

S. Mehta ◽

D. Rice ◽

A. McIntyre ◽

H. Getty ◽

M. Speechley ◽

...

Keyword(s):

Chronic Pain ◽

Personality Traits ◽

Clinical Outcomes ◽

High Risk Group ◽

Comprehensive Treatment ◽

Cluster Membership ◽

Significant Difference ◽

Secondary Objective ◽

The Difference ◽

And Coping

Objective.The current study attempted to identify and characterize distinct CP subgroups based on their level of dispositional personality traits. The secondary objective was to compare the difference among the subgroups in mood, coping, and disability.Methods.Individuals with chronic pain were assessed for demographic, psychosocial, and personality measures. A two-step cluster analysis was conducted in order to identify distinct subgroups of patients based on their level of personality traits. Differences in clinical outcomes were compared using the multivariate analysis of variance based on cluster membership.Results.In 229 participants, three clusters were formed. No significant difference was seen among the clusters on patient demographic factors including age, sex, relationship status, duration of pain, and pain intensity. Those with high levels of dispositional personality traits had greater levels of mood impairment compared to the other two groups (p<0.05). Significant difference in disability was seen between the subgroups.Conclusions.The study identified a high risk group of CP individuals whose level of personality traits significantly correlated with impaired mood and coping. Use of pharmacological treatment alone may not be successful in improving clinical outcomes among these individuals. Instead, a more comprehensive treatment involving psychological treatments may be important in managing the personality traits that interfere with recovery.

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A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data

10.1101/511626 ◽

2019 ◽

Cited By ~ 2

Author(s):

Suhas Srinivasan ◽

Nathan T. Johnson ◽

Dmitry Korkin

Keyword(s):

Deep Learning ◽

Single Cell ◽

Clustering Algorithm ◽

Hybrid Approach ◽

Feature Learning ◽

Specific Cell ◽

Clustering Methods ◽

Model Based Clustering ◽

Clustering And Classification ◽

Living Organisms

AbstractSingle-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. It routinely uses machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amounts of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. Here, we develop a new hybrid approach, Deep Unsupervised Single-cell Clustering (DUSC), that integrates feature generation based on a deep learning architecture with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. The method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.

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Molecular diversity and lineage commitment of human interneuron progenitors

10.1101/2021.05.13.444045 ◽

2021 ◽

Author(s):

Dmitry Velmeshev ◽

Manideep Chavali ◽

Tomasz Jan Nowakowski ◽

Mohini Bhade ◽

Simone Mayer ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Autism Spectrum ◽

Lineage Commitment ◽

Proper Function ◽

Specific Gene ◽

Specific Cell ◽

Cortical Interneurons ◽

Human Specific ◽

Interneuron Development

Cortical interneurons are indispensable for proper function of neocortical circuits. Changes in interneuron development and function are implicated in human disorders, such as autism spectrum disorder and epilepsy. In order to understand human-specific features of cortical development as well as the origins of neurodevelopmental disorders it is crucial to identify the molecular programs underlying human interneuron development and subtype specification. Recent studies have explored gene expression programs underlying mouse interneuron specification and maturation. We applied single-cell RNA sequencing to samples of second trimester human ganglionic eminence and developing cortex to identify molecularly defined subtypes of human interneuron progenitors and immature interneurons. In addition, we integrated this data from the developing human ganglionic eminences and neocortex with single-nucleus RNA-seq of adult cortical interneurons in order to elucidate dynamic molecular changes associated with commitment of progenitors and immature interneurons to mature interneuron subtypes. By comparing our data with published mouse single-cell genomic data, we discover a number of divergent gene expression programs that distinguish human interneuron progenitors from mouse. Moreover, we find that a number of transcription factors expressed during prenatal development become restricted to adult interneuron subtypes in the human but not the mouse, and these adult interneurons express species- and lineage-specific cell adhesion and synaptic genes. Therefore, our study highlights that despite the similarity of main principles of cortical interneuron development and lineage commitment between mouse and human, human interneuron genesis and subtype specification is guided by species-specific gene programs, contributing to human-specific features of cortical inhibitory interneurons.

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Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

eLife ◽

10.7554/elife.49501 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 17

Author(s):

Prashant Rajbhandari ◽

Douglas Arneson ◽

Sydney K Hart ◽

In Sook Ahn ◽

Graciel Diamante ◽

...

Keyword(s):

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Single Cell Analysis ◽

Metabolic Response ◽

Cell Types ◽

Specific Cell ◽

Beneficial Effects ◽

Thermogenic Adipocytes ◽

And Function

Immune cells are vital constituents of the adipose microenvironment that influence both local and systemic lipid metabolism. Mice lacking IL10 have enhanced thermogenesis, but the roles of specific cell types in the metabolic response to IL10 remain to be defined. We demonstrate here that selective loss of IL10 receptor α in adipocytes recapitulates the beneficial effects of global IL10 deletion, and that local crosstalk between IL10-producing immune cells and adipocytes is a determinant of thermogenesis and systemic energy balance. Single Nuclei Adipocyte RNA-sequencing (SNAP-seq) of subcutaneous adipose tissue defined a metabolically-active mature adipocyte subtype characterized by robust expression of genes involved in thermogenesis whose transcriptome was selectively responsive to IL10Rα deletion. Furthermore, single-cell transcriptomic analysis of adipose stromal populations identified lymphocytes as a key source of IL10 production in response to thermogenic stimuli. These findings implicate adaptive immune cell-adipocyte communication in the maintenance of adipose subtype identity and function.

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IMMU-51. SINGLE-CELL ANALYSIS OF TUMOR-ASSOCIATED MICROGLIA AND MACROPHAGES FROM HUMAN GLIOBLASTOMA

Neuro-Oncology ◽

10.1093/neuonc/noab196.410 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi104-vi104

Author(s):

Atul Anand ◽

Rikke Sick Andersen ◽

Mark Burton ◽

Dylan Scott Lykke Harwood ◽

Frantz Rom Poulsen ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Primary Brain Tumor ◽

Tumor Type ◽

Cell Analysis ◽

Data Set ◽

Human Glioblastoma ◽

Glioblastoma Patient ◽

Single Cell Rna Sequencing ◽

Therapeutic Problem

Abstract Patients with glioblastoma, the most frequent and malignant primary brain tumor type, have a poor prognosis with a median survival of 14 months. A major therapeutic problem is chemoresistance. In surgically removed glioblastoma tissue, tumor-associated microglia and macrophages (TAMs) constitute up to 30 % of the total cells. TAMs are capable of secreting cytokines, chemokines and growth factors, thereby influencing the tumor microenvironment. However, the existence of different TAM subtypes and their role in glioblastoma is not fully comprehended and rarely considered therapeutically. This could explain why many glioblastoma clinical trials fail despite of promising preclinical results. This project aims to interrogate the existence and characteristics of different TAM subtypes in human glioblastoma biopsies in order to identify novel subpopulations and therapeutic targets. To study the heterogeneity in TAMs, CD11b+ cells were isolated from glioblastoma patient′s tissue, and single-cell RNA sequencing was performed using the 10X Genomics Chromium platform for single-cell generation and an Illumina NovaSeq6000 system for sequencing. We have sequenced TAMs from three glioblastomas and CD11b+ cells from brain tissue adjacent to two brain metastases samples. In the filtered data set of almost 71,000 CD11b+ cells, we were able to identify recently described TAM populations, such as an interferon-induced, a phagocytic, a hypoxic and a proliferating subset. Interestingly, we also discovered potential novel TAM subsets, such as a pro-angiogenic subset. We have detected a TAM population which is more complex than the established M1 and M2 phenotypes, constituting novel TAM subsets. We are currently investigating these findings to validate specific markers associated with these subpopulations, and for the identification of novel clinically relevant targets.

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