Single cell-derived spheroids capture the self-renewing subpopulations of metastatic ovarian cancer

AbstractHigh Grade Serous Ovarian cancer (HGSOC) is a major unmet need in oncology, due to its precocious dissemination and the lack of meaningful human models for the investigation of disease pathogenesis in a patient-specific manner. To overcome this roadblock, we present a new method to isolate and grow single cells directly from patients’ metastatic ascites, establishing the conditions for propagating them as 3D cultures that we refer to as single cell-derived metastatic ovarian cancer spheroids (sMOCS). By single cell RNA sequencing (scRNAseq) we define the cellular composition of metastatic ascites and trace its propagation in 2D and 3D culture paradigms, finding that sMOCS retain and amplify key subpopulations from the original patients’ samples and recapitulate features of the original metastasis that do not emerge from classical 2D culture, including retention of individual patients’ specificities. By enabling the enrichment of uniquely informative cell subpopulations from HGSOC metastasis and the clonal interrogation of their diversity at the functional and molecular level, this method provides a powerful instrument for precision oncology in ovarian cancer.

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Single cell derived organoids capture the self-renewing subpopulations of metastatic ovarian cancer

10.1101/484121 ◽

2018 ◽

Cited By ~ 2

Author(s):

Tania Velletri ◽

Emanuele Carlo Villa ◽

Michela Lupia ◽

Pietro Lo Riso ◽

Raffaele Luongo ◽

...

Keyword(s):

Ovarian Cancer ◽

Single Cell ◽

Molecular Level ◽

Single Cells ◽

Unmet Need ◽

3D Culture ◽

Patient Specific ◽

Precision Oncology ◽

Cell Subpopulations ◽

2D And 3D

AbstractHigh Grade Serous Ovarian cancer (HGSOC) is a major unmet need in oncology, due to its precocious dissemination and the lack of meaningful human models for the investigation of disease pathogenesis in a patient-specific manner. To overcome this roadblock, we present a new method to isolate and grow single cells directly from patients’ ascites, establishing the conditions for propagating them as single-cell derived ovarian cancer organoids (scOCOs). By single cell RNA sequencing (scRNAseq) we define the cellular composition of metastatic ascites and trace its propagation in 2D and 3D culture paradigms, finding that scOCOs retain and amplify key subpopulations from the original patients’ samples and recapitulate features of the original metastasis that do not emerge from classical 2D culture, including retention of individual patients’ specificities. By enabling the enrichment of uniquely informative cell subpopulations from HGSOC metastasis and the clonal interrogation of their diversity at the functional and molecular level, this method transforms the prospects of precision oncology for ovarian cancer.

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Phenotypic Characterization by Mass Cytometry of the Microenvironment in Ovarian Cancer and Impact of Tumor Dissociation Methods

Cancers ◽

10.3390/cancers13040755 ◽

2021 ◽

Vol 13 (4) ◽

pp. 755

Author(s):

Shamundeeswari Anandan ◽

Liv Cecilie V. Thomsen ◽

Stein-Erik Gullaksen ◽

Tamim Abdelaal ◽

Katrin Kleinmanns ◽

...

Keyword(s):

Ovarian Cancer ◽

Single Cell ◽

Single Cells ◽

Depth Profiling ◽

Patient Specific ◽

Phenotypic Characterization ◽

Gynecological Malignancy ◽

Tissue Handling ◽

Tissue Dissociation

Improved molecular dissection of the tumor microenvironment (TME) holds promise for treating high-grade serous ovarian cancer (HGSOC), a gynecological malignancy with high mortality. Reliable disease-related biomarkers are scarce, but single-cell mapping of the TME could identify patient-specific prognostic differences. To avoid technical variation effects, however, tissue dissociation effects on single cells must be considered. We present a novel Cytometry by Time-of-Flight antibody panel for single-cell suspensions to identify individual TME profiles of HGSOC patients and evaluate the effects of dissociation methods on results. The panel was developed utilizing cell lines, healthy donor blood, and stem cells and was applied to HGSOC tissues dissociated by six methods. Data were analyzed using Cytobank and X-shift and illustrated by t-distributed stochastic neighbor embedding plots, heatmaps, and stacked bar and error plots. The panel distinguishes the main cellular subsets and subpopulations, enabling characterization of individual TME profiles. The dissociation method affected some immune (n = 1), stromal (n = 2), and tumor (n = 3) subsets, while functional marker expressions remained comparable. In conclusion, the panel can identify subsets of the HGSOC TME and can be used for in-depth profiling. This panel represents a promising profiling tool for HGSOC when tissue handling is considered.

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Identification of Specific Cell Subpopulations and Marker Genes in Ovarian Cancer Using Single-Cell RNA Sequencing

BioMed Research International ◽

10.1155/2021/1005793 ◽

2021 ◽

Vol 2021 ◽

pp. 1-27

Author(s):

Yan Li ◽

Juan Wang ◽

Fang Wang ◽

Chengzhen Gao ◽

Yuanyuan Cao ◽

...

Keyword(s):

Ovarian Cancer ◽

Single Cell ◽

Differentially Expressed Genes ◽

Single Cells ◽

Differentially Expressed ◽

Ovarian Cancer Cells ◽

Marker Genes ◽

Specific Cell ◽

Rna Seq ◽

Cell Subpopulations

Objective. Ovarian cancer is the deadliest gynaecological cancer globally. In our study, we aimed to analyze specific cell subpopulations and marker genes among ovarian cancer cells by single-cell RNA sequencing (RNA-seq). Methods. Single-cell RNA-seq data of 66 high-grade serous ovarian cancer cells were employed from the Gene Expression Omnibus (GEO). Using the Seurat package, we performed quality control to remove cells with low quality. After normalization, we detected highly variable genes across the single cells. Then, principal component analysis (PCA) and cell clustering were performed. The marker genes in different cell clusters were detected. A total of 568 ovarian cancer samples and 8 normal ovarian samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes were identified according to ∣ log 2 fold change FC ∣ > 1 and adjusted p value <0.05. To explore potential biological processes and pathways, functional enrichment analyses were performed. Furthermore, survival analyses of differentially expressed marker genes were performed. Results. After normalization, 6000 highly variable genes were identified across the single cells. The cells were divided into 3 cell populations, including G1, G2M, and S cell cycles. A total of 1,124 differentially expressed genes were identified in ovarian cancer samples. These differentially expressed genes were enriched in several pathways associated with cancer, such as metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway. Furthermore, marker genes, STAT1, ANP32E, GPRC5A, and EGFL6, were highly expressed in ovarian cancer, while PMP22, FBXO21, and CYB5R3 were lowly expressed in ovarian cancer. These marker genes were positively associated with prognosis of ovarian cancer. Conclusion. Our findings revealed specific cell subpopulations and marker genes in ovarian cancer using single-cell RNA-seq, which provided a novel insight into the heterogeneity of ovarian cancer.

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Generation of 2.5D lung bud organoid from human induced pluripotent stem cell

Clinical Hemorheology and Microcirculation ◽

10.3233/ch-219111 ◽

2021 ◽

pp. 1-14

Author(s):

Xun Xu ◽

Yan Nie ◽

Weiwei Wang ◽

Imran Ullah ◽

Wing Tai Tung ◽

...

Keyword(s):

Single Cell ◽

Disease Modeling ◽

3D Culture ◽

Induced Pluripotent Stem Cell ◽

Patient Specific ◽

Homogeneous Distribution ◽

Cell Seeding ◽

Differentiation Potential ◽

Induced Pluripotent ◽

Defined Culture

Human induced pluripotent stem cells (hiPSCs) are a promising cell source to generate the patient-specific lung organoid given their superior differentiation potential. However, the current 3D cell culture approach is tedious and time-consuming with a low success rate and high batch-to-batch variability. Here, we explored the establishment of lung bud organoids by systematically adjusting the initial confluence levels and homogeneity of cell distribution. The efficiency of single cell seeding and clump seeding was compared. Instead of the traditional 3D culture, we established a 2.5D organoid culture to enable the direct monitoring of the internal structure via microscopy. It was found that the cell confluence and distribution prior to induction were two key parameters, which strongly affected hiPSC differentiation trajectories. Lung bud organoids with positive expression of NKX 2.1, in a single-cell seeding group with homogeneously distributed hiPSCs at 70%confluence (SC_70%_hom) or a clump seeding group with heterogeneously distributed cells at 90%confluence (CL_90%_het), can be observed as early as 9 days post induction. These results suggest that a successful lung bud organoid formation with single-cell seeding of hiPSCs requires a moderate confluence and homogeneous distribution of cells, while high confluence would be a prominent factor to promote the lung organoid formation when seeding hiPSCs as clumps. 2.5D organoids generated with defined culture conditions could become a simple, efficient, and valuable tool facilitating drug screening, disease modeling and personalized medicine.

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Epigenetic Therapy Augments Classic Chemotherapy in Suppressing the Growth of 3D High-Grade Serous Ovarian Cancer Spheroids over an Extended Period of Time

Biomolecules ◽

10.3390/biom11111711 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1711

Author(s):

Michelle Bilbao ◽

Chelsea Katz ◽

Stephanie L. Kass ◽

Devon Smith ◽

Krystal Hunter ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Cells ◽

3D Culture ◽

Extended Period ◽

Recurrent Ovarian Cancer ◽

Epigenetic Therapy ◽

Ovarian Cancer Cells ◽

High Grade ◽

Serous Ovarian Cancer ◽

Cancer Spheroids

Recurrent high-grade serous ovarian cancer (HGSC) is clinically very challenging and prematurely shortens patients’ lives. Recurrent ovarian cancer is characterized by high tumor heterogeneity; therefore, it is susceptible to epigenetic therapy in classic 2D tissue culture and rodent models. Unfortunately, this success has not translated well into clinical trials. Utilizing a 3D spheroid model over a period of weeks, we were able to compare the efficacy of classic chemotherapy and epigenetic therapy on recurrent ovarian cancer cells. Unexpectedly, in our model, a single dose of paclitaxel alone caused the exponential growth of recurrent high-grade serous epithelial ovarian cancer over a period of weeks. In contrast, this effect is not only opposite under treatment with panobinostat, but panobinostat reverses the repopulation of cancer cells following paclitaxel treatment. In our model, we also demonstrate differences in the drug-treatment sensitivity of classic chemotherapy and epigenetic therapy. Moreover, 3D-derived ovarian cancer cells demonstrate induced proliferation, migration, invasion, cancer colony formation and chemoresistance properties after just a single exposure to classic chemotherapy. To the best of our knowledge, this is the first evidence demonstrating a critical contrast between short and prolonged post-treatment outcomes following classic chemotherapy and epigenetic therapy in recurrent high-grade serous ovarian cancer in 3D culture.

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Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

Physiological Genomics ◽

10.1152/physiolgenomics.00065.2020 ◽

2020 ◽

Vol 52 (10) ◽

pp. 468-477

Author(s):

Alexander C. Zambon ◽

Tom Hsu ◽

Seunghee Erin Kim ◽

Miranda Klinck ◽

Jennifer Stowe ◽

...

Keyword(s):

Cell Cycle ◽

Single Cell ◽

Single Cell Analysis ◽

Imaging System ◽

Single Cells ◽

Cell Types ◽

Cell Analysis ◽

Mcf7 Cells ◽

Genomic Studies ◽

Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

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Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2005990117 ◽

2020 ◽

Vol 117 (46) ◽

pp. 28784-28794

Author(s):

Sisi Chen ◽

Paul Rivaud ◽

Jong H. Park ◽

Tiffany Tsou ◽

Emeric Charles ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Large Scale ◽

Probabilistic Models ◽

Single Cells ◽

Measurement Techniques ◽

Patient Specific ◽

Cell Populations ◽

Cell Measurement ◽

The Impact

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

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Single-cell ATAC-seq Signal Extraction and Enhancement with SCATE

10.1101/795609 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zhicheng Ji ◽

Weiqiang Zhou ◽

Hongkai Ji

Keyword(s):

Single Cell ◽

State Of The Art ◽

Single Cells ◽

Regulatory Elements ◽

Single Cell Sequencing ◽

Statistical Framework ◽

Genome Wide ◽

Cell Subpopulations ◽

Accessible Chromatin ◽

Regulatory Landscape

AbstractSingle-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscape in single cells. Single-cell ATAC-seq data are sparse and noisy. Analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We show that using SCATE, one can better reconstruct the regulatory landscape of a heterogeneous sample.

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High-throughput RNA sequencing of paraformaldehyde-fixed single cells

Nature Communications ◽

10.1038/s41467-021-25871-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Hoang Van Phan ◽

Michiel van Gent ◽

Nir Drayman ◽

Anindita Basu ◽

Michaela U. Gack ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cells ◽

Human Tumor ◽

Viral Reactivation ◽

Rna Integrity ◽

High Throughput Method ◽

Tumor Virus ◽

Cell Subpopulations

AbstractSingle-cell transcriptomic studies that require intracellular protein staining, rare cell sorting, or inactivation of infectious pathogens are severely limited. This is because current high-throughput single-cell RNA sequencing methods are either incompatible with or necessitate laborious sample preprocessing for paraformaldehyde treatment, a common tissue and cell fixation and preservation technique. Here we present FD-seq (Fixed Droplet RNA sequencing), a high-throughput method for droplet-based RNA sequencing of paraformaldehyde-fixed, permeabilized and sorted single cells. We show that FD-seq preserves the RNA integrity and relative gene expression levels after fixation and permeabilization. Furthermore, FD-seq can detect a higher number of genes and transcripts than methanol fixation. We first apply FD-seq to analyze a rare subpopulation of cells supporting lytic reactivation of the human tumor virus KSHV, and identify TMEM119 as a potential host factor that mediates viral reactivation. Second, we find that infection with the human betacoronavirus OC43 leads to upregulation of pro-inflammatory pathways in cells that are exposed to the virus but fail to express high levels of viral genes. FD-seq thus enables integrating phenotypic with transcriptomic information in rare cell subpopulations, and preserving and inactivating pathogenic samples.

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Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient

10.21203/rs.3.rs-57913/v2 ◽

2020 ◽

Author(s):

HARIPRIYA HARIKUMAR ◽

Thomas P Quinn ◽

Santu Rana ◽

Sunil Gupta ◽

Svetha Venkatesh

Keyword(s):

Single Cell ◽

Drug Response ◽

Single Cells ◽

Biological Data ◽

Patient Specific ◽

Expression Data ◽

Drug Responses ◽

Dual Channel ◽

Cell Gene ◽

Cell Networks

Abstract Background: The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient.Methods: We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses.Conclusions: Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

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