Learning time-varying information flow from single-cell epithelial to mesenchymal transition data

ABSTRACTA problem ubiquitous in almost all scientific areas is escape from a metastable state, or relaxation from one stationary distribution to a new one1. More than a century of studies lead to celebrated theoretical and computational developments such as the transition state theory and reactive flux formulation. Modern transition path sampling and transition path theory focus on an ensemble of trajectories that connect the initial and final states in a state space2, 3. However, it is generally unfeasible to experimentally observe these trajectories in multiple dimensions and compare to theoretical results. Here we report and analyze single cell trajectories of human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT) in a combined morphology and protein texture space obtained through time lapse imaging. From the trajectories we identify parallel reaction paths with corresponding reaction coordinates and quasi-potentials. Studying cell phenotypic transition dynamics will provide testing grounds for nonequilibrium reaction rate theories.

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Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas

Frontiers in Genetics ◽

10.3389/fgene.2020.605012 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiangtian Yu ◽

XiaoYong Pan ◽

ShiQi Zhang ◽

Yu-Hang Zhang ◽

Lei Chen ◽

...

Keyword(s):

Drug Resistance ◽

Single Cell ◽

Epithelial To Mesenchymal Transition ◽

Expression Patterns ◽

Cancer Recurrence ◽

Functional Enrichment ◽

Support Vector ◽

Pathogenic Potential ◽

Mesenchymal Transition ◽

Cell Expression

Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT.

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Faculty Opinions recommendation of A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.736535842.793575131 ◽

2020 ◽

Author(s):

Stephen Weiss

Keyword(s):

Single Cell ◽

Epithelial To Mesenchymal Transition ◽

Genetic Screen ◽

Mesenchymal Transition ◽

The Continuum

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Probing single-cell metabolism reveals prognostic value of highly metabolically active circulating stromal cells in prostate cancer

Science Advances ◽

10.1126/sciadv.aaz3849 ◽

2020 ◽

Vol 6 (40) ◽

pp. eaaz3849

Author(s):

Francesca Rivello ◽

Kinga Matuła ◽

Aigars Piruska ◽

Minke Smits ◽

Niven Mehra ◽

...

Keyword(s):

Prostate Cancer ◽

Single Cell ◽

Cancer Patients ◽

Stromal Cells ◽

Metastatic Prostate Cancer ◽

Epithelial To Mesenchymal Transition ◽

Therapy Response ◽

Sequencing Analysis ◽

Label Free ◽

Mesenchymal Transition

Despite their important role in metastatic disease, no general method to detect circulating stromal cells (CStCs) exists. Here, we present the Metabolic Assay-Chip (MA-Chip) as a label-free, droplet-based microfluidic approach allowing single-cell extracellular pH measurement for the detection and isolation of highly metabolically active cells (hm-cells) from the tumor microenvironment. Single-cell mRNA-sequencing analysis of the hm-cells from metastatic prostate cancer patients revealed that approximately 10% were canonical EpCAM+ hm-CTCs, 3% were EpCAM− hm-CTCs with up-regulation of prostate-related genes, and 87% were hm-CStCs with profiles characteristic for cancer-associated fibroblasts, mesenchymal stem cells, and endothelial cells. Kaplan-Meier analysis shows that metastatic prostate cancer patients with more than five hm-cells have a significantly poorer survival probability than those with zero to five hm-cells. Thus, prevalence of hm-cells is a prognosticator of poor outcome in prostate cancer, and a potentially predictive and therapy response biomarker for agents cotargeting stromal components and preventing epithelial-to-mesenchymal transition.

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A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition

Nature Genetics ◽

10.1038/s41588-019-0489-5 ◽

2019 ◽

Vol 51 (9) ◽

pp. 1389-1398 ◽

Cited By ~ 34

Author(s):

José L. McFaline-Figueroa ◽

Andrew J. Hill ◽

Xiaojie Qiu ◽

Dana Jackson ◽

Jay Shendure ◽

...

Keyword(s):

Single Cell ◽

Epithelial To Mesenchymal Transition ◽

Genetic Screen ◽

Mesenchymal Transition ◽

The Continuum

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Integrated computational analyses reveal novel insights into the stromal microenvironment of SHH-subtype medulloblastoma

Scientific Reports ◽

10.1038/s41598-021-00244-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alexander P. Landry ◽

Nardin Samuel ◽

Julian Spears ◽

Zsolt Zador

Keyword(s):

Single Cell ◽

Epithelial To Mesenchymal Transition ◽

Metastatic Potential ◽

Tumour Microenvironment ◽

Biologically Active ◽

Computational Techniques ◽

Mesenchymal Transition ◽

Stromal Microenvironment ◽

Computational Analyses

AbstractMedulloblastoma is the most common malignant brain tumour of childhood. While our understanding of this disease has progressed substantially in recent years, the role of tumour microenvironment remains unclear. Given the increasing role of microenvironment-targeted therapeutics in other cancers, this study was aimed at further exploring its role in medulloblastoma. Multiple computational techniques were used to analyze open-source bulk and single cell RNA seq data from primary samples derived from all subgroups of medulloblastoma. Gene expression is used to infer stromal subpopulations, and network-based approaches are used to identify potential therapeutic targets. Bulk data was obtained from 763 medulloblastoma samples and single cell data from an additional 7241 cells from 23 tumours. Independent bulk (285 tumours) and single cell (32,868 cells from 29 tumours) validation cohorts were used to verify results. The SHH subgroup was found to be enriched in stromal activity, including the epithelial-to-mesenchymal transition, while group 3 is comparatively stroma-suppressed. Several receptor and ligand candidates underlying this difference are identified which we find to correlate with metastatic potential of SHH medulloblastoma. Additionally, a biologically active gradient is detected within SHH medulloblastoma, from “stroma-active” to “stroma-suppressed” cells which may have relevance to targeted therapy. This study serves to further elucidate the role of the stromal microenvironment in SHH-subgroup medulloblastoma and identify novel treatment possibilities for this challenging disease.

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Single-cell resolution of MET and EMT programs during zebrafish fin regeneration

10.1101/2021.04.29.440827 ◽

2021 ◽

Author(s):

W. Joyce Tang ◽

Claire J. Watson ◽

Theresa Olmstead ◽

Christopher H. Allan ◽

Ronald Y. Kwon

Keyword(s):

Single Cell ◽

Osteoblast Differentiation ◽

Epithelial To Mesenchymal Transition ◽

Limb Regeneration ◽

Osteoblastic Cells ◽

Fin Regeneration ◽

Mesenchymal Transition ◽

Mesenchymal To Epithelial Transition ◽

Amputation Stump ◽

Single Cell Rna Sequencing

SUMMARYWhile humans have limited potential for limb regeneration, some vertebrates can regenerate bony appendages following amputation. During zebrafish fin regeneration, mature osteoblasts at the amputation stump dedifferentiate and migrate to the blastema, where they re-enter the cell cycle and then re-differentiate to form new bone. Osteoblastic cells exhibit dual mesenchymal and epithelial characteristics during fin regeneration, however little is known about why or how this occurs. Using single-cell RNA-sequencing, we found osteoprogenitors are enriched with components associated with the epithelial-to-mesenchymal transition (EMT) and its reverse, mesenchymal-to-epithelial transition (MET). In trajectory analyses, osteoblastic cells solely expressed EMT components, or transiently expressed MET components prior to expressing those for EMT. We found that cdh11, a cancer EMT marker, is expressed during osteoblast dedifferentiation. We also found that esrp1, a regulator of alternative splicing in epithelial cells whose expression is important for MET, is expressed in a subset of osteoprogenitors during outgrowth. This study provides a valuable single cell resource for the study of osteoblast differentiation during zebrafish fin regeneration, and identifies MET- and EMT-associated components which may be important for successful appendage regeneration.

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Golgi stabilization, not its front-rear bias, is associated with EMT-enhanced fibrillar migration

10.1101/308536 ◽

2018 ◽

Author(s):

Robert J. Natividad ◽

Mark L. Lalli ◽

Senthil K. Muthuswamy ◽

Anand R. Asthagiri

Keyword(s):

Cell Migration ◽

Single Cell ◽

Epithelial To Mesenchymal Transition ◽

Mammary Epithelial Cells ◽

Mammary Epithelial ◽

Migration Speed ◽

Mesenchymal Transition ◽

Positional Bias ◽

Persistent Cell ◽

Posterior Position

ABSTRACTEpithelial-to-mesenchymal transition (EMT) and maturation of collagen fibrils in the tumor microenvironment play a significant role in cancer cell invasion and metastasis. Confinement along fiber-like tracks enhances cell migration. To what extent and in what manner EMT further promotes migration in a microenvironment already conducive to migration is poorly understood. Here, we show that TGFβ-mediated EMT significantly enhances migration on fiber-like micropatterned tracks of collagen, doubling migration speed and quadrupling persistence relative to untreated mammary epithelial cells. Thus, cell-intrinsic EMT and extrinsic fibrillar tracks have non-redundant effects on motility. To better understand EMT-enhanced fibrillar migration, we investigated the regulation of Golgi positioning, which is involved in front-rear polarization and persistent cell migration. Confinement along fiber-like tracks has been reported to favor posterior Golgi positioning, whereas anterior positioning is observed during 2d wound healing. While EMT also regulates cell polarity, little is known about its effect on Golgi positioning. Here, we show that EMT induces a 2:1 rearward bias in Golgi positioning; however, positional bias explains less than 5% of single-cell variability in migration speed and persistence. Meanwhile, EMT significantly stabilizes Golgi positioning. Cells that enhance migration in response to TGFβ maintain Golgi position for 3-4 fold longer than untreated counterparts, irrespective of whether the Golgi is ahead or behind the nucleus. In fact, 35% of cells that respond to TGFβ exhibit a fully-committed Golgi phenotype with the organelle either in the anterior or posterior position for over 90% of the time. Furthermore, single-cell differences in Golgi stability capture up to 30% of variations in migration speed and persistence. These results lead us to propose that the Golgi is part of a core physical scaffold that distributes cell-generated forces necessary for migration. A stable scaffold more consistently, and therefore more productively, distributes forces over time, leading to efficient migration.

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Learning Edge Rewiring in EMT From Single Cell Data

10.1101/155028 ◽

2017 ◽

Cited By ~ 2

Author(s):

Smita Krishnaswamy ◽

Nevena Zivanovic ◽

Roshan Sharma ◽

Dana Pe’er ◽

Bernd Bodenmiller

Keyword(s):

Single Cell ◽

Breast Cancer Cells ◽

Regulatory Networks ◽

Epithelial To Mesenchymal Transition ◽

Mass Cytometry ◽

Mesenchymal Transition ◽

Dynamic Modulation ◽

Static Interaction ◽

Cell Data ◽

Time Point

AbstractCellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in gene expression and protein confirmations. However, typical computational approaches treat them as static interaction networks derived from a single experimental time point. Here, we provide a method for learning the dynamic modulation, or rewiring of pairwise relationships (edges) from a static single-cell data. We use the epithelial-to-mesenchymal transition (EMT) in murine breast cancer cells as a model system, and measure mass cytometry data three days after induction of the transition by TGFβ. We take advantage of transitional rate variability between cells in the data by deriving a pseudo-time EMT trajectory. Then we propose methods for visualizing and quantifying time-varying edge behavior over the trajectory and use these methods: TIDES (Trajectory Imputed DREMI scores), and measure of edge dynamism (3DDREMI) to predict and validate the effect of drug perturbations on EMT.

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Urine single cell RNA-sequencing in focal segmental glomerulosclerosis reveals inflammatory signatures in immune cells and podocytes

10.1101/2020.10.18.343285 ◽

2020 ◽

Author(s):

Khun Zaw Latt ◽

Jurgen Heymann ◽

Joseph H. Jessee ◽

Avi Z. Rosenberg ◽

Celine C. Berthier ◽

...

Keyword(s):

Nephrotic Syndrome ◽

Single Cell ◽

Rna Sequencing ◽

Focal Segmental Glomerulosclerosis ◽

Immune Cells ◽

Epithelial To Mesenchymal Transition ◽

Mesenchymal Transition ◽

Segmental Glomerulosclerosis ◽

Expression Of Genes ◽

Single Cell Rna Sequencing

AbstractThe diagnosis of focal segmental glomerulosclerosis (FSGS) requires a renal biopsy, which is invasive and can be problematic in children and in some adults. We used single cell RNA-sequencing to explore disease-related cellular signatures in 23 urine samples from 12 FSGS subjects. We identified immune cells, predominantly monocytes, and renal epithelial cells, including podocytes. Analysis revealed M1 and M2 monocyte subsets, and podocytes showing high expression of genes for epithelial-to-mesenchymal transition (EMT). We confirmed M1 and M2 gene signatures using published monocyte/macrophage data from lupus nephritis and cancer. Using renal transcriptomic data from the Nephrotic Syndrome Study Network (NEPTUNE), we found that urine cell immune and EMT signature genes showed higher expression in FSGS biopsies compared to minimal change disease biopsies. These results suggest that urine cell profiling may serve as a diagnostic and prognostic tool in nephrotic syndrome and aid in identifying novel biomarkers and developing personalized therapeutic strategies.

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