Hand2 delineates mesothelium progenitors and is reactivated in mesothelioma

AbstractThe mesothelium forms epithelial membranes that line the bodies cavities and surround the internal organs. Mesothelia widely contribute to organ homeostasis and regeneration, and their dysregulation can result in congenital anomalies of the viscera, ventral wall defects, and mesothelioma tumors. Nonetheless, the embryonic ontogeny and developmental regulation of mesothelium formation has remained uncharted. Here, we combine genetic lineage tracing, in toto live imaging, and single-cell transcriptomics in zebrafish to track mesothelial progenitor origins from the lateral plate mesoderm (LPM). Our single-cell analysis uncovers a post-gastrulation gene expression signature centered on hand2 that delineates distinct progenitor populations within the forming LPM. Combining gene expression analysis and imaging of transgenic reporter zebrafish embryos, we chart the origin of mesothelial progenitors to the lateral-most, hand2-expressing LPM and confirm evolutionary conservation in mouse. Our time-lapse imaging of transgenic hand2 reporter embryos captures zebrafish mesothelium formation, documenting the coordinated cell movements that form pericardium and visceral and parietal peritoneum. We establish that the primordial germ cells migrate associated with the forming mesothelium as ventral migration boundary. Functionally, hand2 mutants fail to close the ventral mesothelium due to perturbed migration of mesothelium progenitors. Analyzing mouse and human mesothelioma tumors hypothesized to emerge from transformed mesothelium, we find de novo expression of LPM-associated transcription factors, and in particular of Hand2, indicating the re-initiation of a developmental transcriptional program in mesothelioma. Taken together, our work outlines a genetic and developmental signature of mesothelial origins centered around Hand2, contributing to our understanding of mesothelial pathologies and mesothelioma.

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Single cell analysis reveals stochastic cell-to-cell variation in stress response and senescence program

Innovation in Aging ◽

10.1093/geroni/igab046.3732 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 1055-1055

Author(s):

Nikolay Burnaevskiy ◽

Alexander Mendenhall

Keyword(s):

Gene Expression ◽

Stress Response ◽

Single Cell ◽

Large Scale ◽

Single Cell Analysis ◽

Human Fibroblasts ◽

Gene Expression Signature ◽

Controlled Environment ◽

Proliferating Cells ◽

Expression Signature

Abstract Cells have various means to respond to molecular stress. Upon stress, proliferating cells can adopt different fates, e.g. commit to apoptosis, go into senescence or recover from stress and resume proliferation, depending on severity of the stress. Proper balance between these modes of response is critical for maintaining tissue homeostasis with age, as both exacerbated and insufficient response can result in pathology. Remarkably, even genetically identical cells of the same type in the controlled environment can exhibit a spectrum of responses to the same stress challenge. We hypothesized that analyzing response of individual cells in controlled environment can help better understand the mechanisms that ensure a balanced response to molecular stress. We used large scale single cell RNA-sequencing to analyze response of individual human fibroblasts to oxidative stress. Consistent with various fates adopted by individual cells upon stress, we observed different transcriptional signatures, that correspond to those fates. Surprisingly, when we specifically analyzed ß-gal+ senescent cells, we still observed transcriptional heterogeneity, with only a subset of cells exhibiting pro-senescent transcriptional signature (e.g. activated p53 and TNF-a pathways) while another subset exhibits a gene expression signature of senescent-like arrest. Hence, we find that in addition to known stress-related fates (apoptosis, senescence, recovery) senescence-like response is heterogeneous with only subset of cells exhibiting expected pro-senescent gene expression signature. Further characterization of heterogeneity of stress response and senescent-like fates will help better understand the mechanisms of homeostatic control in the face of molecular stress and aging.

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Redefining De Novo Gammaherpesvirus Infection Through High-Dimensional, Single-Cell Analysis of Virus and Host

10.1101/2020.08.11.203117 ◽

2020 ◽

Author(s):

Jennifer N. Berger ◽

Bridget Sanford ◽

Abigail K. Kimball ◽

Lauren M. Oko ◽

Rachael E. Kaspar ◽

...

Keyword(s):

Gene Expression ◽

Virus Infection ◽

Single Cell ◽

Single Cell Analysis ◽

De Novo ◽

Viral Gene Expression ◽

High Dimensional ◽

Viral Gene ◽

Cell Analysis ◽

Infected Cells

SUMMARYVirus infection is frequently characterized using bulk cell populations. How these findings correspond to infection in individual cells remains unclear. Here, we integrate high-dimensional single-cell approaches to quantify viral and host RNA and protein expression signatures using de novo infection with a well-characterized model gammaherpesvirus. While infected cells demonstrated genome-wide transcription, individual cells revealed pronounced variation in gene expression, with only 9 of 80 annotated viral open reading frames uniformly expressed in all cells, and a 1000-fold variation in viral RNA expression between cells. Single-cell analysis further revealed positive and negative gene correlations, many uniquely present in a subset of cells. Beyond variation in viral gene expression, individual cells demonstrated a pronounced, dichotomous signature in host gene expression, revealed by measuring host RNA abundance and post-translational protein modifications. These studies provide a resource for the high-dimensional analysis of virus infection, and a conceptual framework to define virus infection as the sum of virus and host responses at the single-cell level.HIGHLIGHTSCyTOF and scRNA-seq identify wide variation in gene expression between infected cells.Host RNA expression and post-translational modifications stratify virus infection.Single cell RNA analysis reveals new relationships in viral gene expression.Simultaneous measurement of virus and host defines distinct infection states.

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Sequential fate-switches in stem-like cells drive the tumorigenic trajectory from human neural stem cells to malignant glioma

Cell Research ◽

10.1038/s41422-020-00451-z ◽

2021 ◽

Author(s):

Xiaofei Wang ◽

Ran Zhou ◽

Yanzhen Xiong ◽

Lingling Zhou ◽

Xiang Yan ◽

...

Keyword(s):

Malignant Glioma ◽

Single Cell ◽

De Novo ◽

Early Stage ◽

Lineage Tracing ◽

Human Neural Stem Cells ◽

Transcriptional Reprogramming ◽

Neural Stem Progenitor Cells ◽

Human Gbm ◽

End Stage

AbstractGlioblastoma (GBM) is an incurable and highly heterogeneous brain tumor, originating from human neural stem/progenitor cells (hNSCs/hNPCs) years ahead of diagnosis. Despite extensive efforts to characterize hNSCs and end-stage GBM at bulk and single-cell levels, the de novo gliomagenic path from hNSCs is largely unknown due to technical difficulties in early-stage sampling and preclinical modeling. Here, we established two highly penetrant hNSC-derived malignant glioma models, which resemble the histopathology and transcriptional heterogeneity of human GBM. Integrating time-series analyses of whole-exome sequencing, bulk and single-cell RNA-seq, we reconstructed gliomagenic trajectories, and identified a persistent NSC-like population at all stages of tumorigenesis. Through trajectory analyses and lineage tracing, we showed that tumor progression is primarily driven by multi-step transcriptional reprogramming and fate-switches in the NSC-like cells, which sequentially generate malignant heterogeneity and induce tumor phenotype transitions. We further uncovered stage-specific oncogenic cascades, and among the candidate genes we functionally validated C1QL1 as a new glioma-promoting factor. Importantly, the neurogenic-to-gliogenic switch in NSC-like cells marks an early stage characterized by a burst of oncogenic alterations, during which transient AP-1 inhibition is sufficient to inhibit gliomagenesis. Together, our results reveal previously undercharacterized molecular dynamics and fate choices driving de novo gliomagenesis from hNSCs, and provide a blueprint for potential early-stage treatment/diagnosis for GBM.

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Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics

Nature Communications ◽

10.1038/s41467-021-21650-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Lars Velten ◽

Benjamin A. Story ◽

Pablo Hernández-Malmierca ◽

Simon Raffel ◽

Daniel R. Leonce ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Cancer Stem Cells ◽

Single Cell ◽

Lineage Tracing ◽

Specific Gene ◽

Leukemic Stem Cells ◽

Hematopoietic Stem ◽

Mutational Status

AbstractCancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with it the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells.

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Cell-to-cell heterogeneity in Escherichia coli stress response originates from pulsatile expression and growth

10.1101/2020.09.14.297101 ◽

2020 ◽

Author(s):

Nadia M. V. Sampaio ◽

Caroline M. Blassick ◽

Jean-Baptiste Lugagne ◽

Mary J. Dunlop

Keyword(s):

Gene Expression ◽

Escherichia Coli ◽

Stress Response ◽

Single Cell ◽

Phenotypic Variability ◽

Growth Dynamics ◽

Time Lapse ◽

Cell Heterogeneity ◽

Temporal Expression ◽

Functional Consequences

AbstractCell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is critical because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found widespread examples of pulsatile expression. Single-cell growth rates were often anti-correlated with gene expression, with changes in growth preceding changes in expression. These pulsatile dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that pulsatile expression and growth dynamics are common in stress response networks and can have direct consequences for survival.

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Generative modeling of single-cell population time series for inferring cell differentiation landscapes

10.1101/2020.08.26.269332 ◽

2020 ◽

Author(s):

Grace H.T. Yeo ◽

Sachit D. Saksena ◽

David K. Gifford

Keyword(s):

Gene Expression ◽

Time Series ◽

Cell Differentiation ◽

Single Cell ◽

Cell Fate ◽

Lineage Tracing ◽

Model Framework ◽

Modeling Framework ◽

Generative Modeling ◽

Model Complex

SummaryExisting computational methods that use single-cell RNA-sequencing for cell fate prediction either summarize observations of cell states and their couplings without modeling the underlying differentiation process, or are limited in their capacity to model complex differentiation landscapes. Thus, contemporary methods cannot predict how cells evolve stochastically and in physical time from an arbitrary starting expression state, nor can they model the cell fate consequences of gene expression perturbations. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from single-cell time-series gene expression data. Our generative model framework provides insight into the process of differentiation and can simulate differentiation trajectories for arbitrary gene expression progenitor states. We validate our method on a recently published experimental lineage tracing dataset that provides observed trajectories. We show that this model is able to predict the fate biases of progenitor cells in neutrophil/macrophage lineages when accounting for cell proliferation, improving upon the best-performing existing method. We also show how a model can predict trajectories for cells not found in the model’s training set, including cells in which genes or sets of genes have been perturbed. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations. PRESCIENT models are able to recover the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation.

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Direct neuronal reprogramming by temporal identity factors

10.1101/2021.07.05.451124 ◽

2021 ◽

Author(s):

Camille Boudreau-Pinsonneault ◽

Awais Javed ◽

Michel Fries ◽

Pierre Mattar ◽

Michel Cayouette

Keyword(s):

Gene Expression ◽

Expression System ◽

Lineage Tracing ◽

Developmental Competence ◽

Rapid Screening ◽

Mouse Retina ◽

Genetic Lineage ◽

Differentiated Cells ◽

Temporal Identity

Temporal identity factors are sufficient to reprogram developmental competence of neural progenitors, but whether they could also reprogram the identity of fully differentiated cells is unknown. To address this question, we designed a conditional gene expression system combined with genetic lineage tracing that allows rapid screening of potential reprogramming factors in the mouse retina. Using this assay, we report that co-expression of the early temporal identity transcription factor Ikzf1, together with Ikzf4, another Ikaros family member, is sufficient to directly convert adult Muller glial cells into neuron-like cells in vivo, without inducing a proliferative progenitor state. scRNA-seq analysis shows that the reprogrammed cells share some transcriptional signatures with both cone photoreceptors and bipolar cells. Furthermore, we show that co-expression of Ikzf1 and Ikzf4 can reprogram mouse embryonic fibroblasts to induced neurons by remodeling chromatin and promoting a neuronal gene expression program. This work uncovers general neuronal reprogramming properties for temporal identity factors in differentiated cells, opening new opportunities for cell therapy development.

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HOXA5 protein expression and genetic fate mapping show lineage restriction in the developing musculoskeletal system

The International Journal of Developmental Biology ◽

10.1387/ijdb.180214jm ◽

2018 ◽

Vol 62 (11-12) ◽

pp. 785-796

Author(s):

Miriam A. Holzman ◽

Jenna M. Bergmann ◽

Maya Feldman ◽

Kim Landry-Truchon ◽

Lucie Jeannotte ◽

...

Keyword(s):

Protein Expression ◽

Hox Genes ◽

Lineage Tracing ◽

Genetic Lineage ◽

Lateral Plate Mesoderm ◽

Embryonic Cells ◽

Connective Tissues ◽

Lateral Plate ◽

Direct Role ◽

Skeletal Patterning

HOX proteins act during development to regulate musculoskeletal morphology. HOXA5 patterns skeletal structures surrounding the cervical-thoracic transition including the vertebrae, ribs, sternum and forelimb girdle. However, the tissue types in which it acts to pattern the skeleton, and the ultimate fates of embryonic cells that activate Hoxa5 expression are unknown. A detailed characterization of HOXA5 expression by immunofluorescence was combined with Cre/LoxP genetic lineage tracing to map the fate of Hoxa5 expressing cells in axial musculoskeletal tissues and in their precursors, the somites and lateral plate mesoderm. HOXA5 protein expression is dynamic and spatially restricted in derivatives of both the lateral plate mesoderm and somites, including a subset of the lateral sclerotome, suggesting a local role in regulating early skeletal patterning. HOXA5 expression persists from somite stages through late development in differentiating skeletal and connective tissues, pointing to a continuous and direct role in skeletal patterning. In contrast, HOXA5 expression is excluded from the skeletal muscle and muscle satellite cell lineages. Furthermore, the descendants of Hoxa5-expressing cells, even after HOXA5 expression has extinguished, never contribute to these lineages. Together, these findings suggest cell autonomous roles for HOXA5 in skeletal development, as well as non-cell autonomous functions in muscle through expression in surrounding connective tissues. They also support the notion that different Hox genes display diverse tissue specificities and locations to achieve their patterning activity.

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Impact of MYC and BCL2 structural variants in tumors of DLBCL morphology and mechanisms of false-negative MYC IHC

Blood ◽

10.1182/blood.2020007193 ◽

2020 ◽

Author(s):

Brett J Collinge ◽

Susana Ben-Neriah ◽

Lauren C. Chong ◽

Merrill Boyle ◽

Aixiang Jiang ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

De Novo ◽

Cell Lymphoma ◽

False Negative ◽

Gene Expression Signature ◽

B Cell Lymphoma ◽

Copy Number Variations ◽

Similar Gene Expression Profile ◽

Double Hit

When the WHO defined high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (HGBL-DH/TH) as a clinical category, rearrangements were the only structural variant (SV) incorporated. An "atypical double-hit" entity has been proposed, encompassing tumors with concurrent MYC and BCL2 SVs other than co-occurring translocations - i.e. copy number variations (CNVs). While the identification of a gene expression signature (DHITsig) shared among tumors harboring MYC and BCL2 rearrangements (HGBL-DH/TH-BCL2) has confirmed a shared underlying biology, the biological implication of MYC and BCL2 CNVs requires further elucidation. We performed a comprehensive analysis of MYC and BCL2 SVs, as determined by fluorescent in situ hybridization (FISH), in a cohort of 802 de novo tumors with diffuse large B-cell lymphoma (DLBCL) morphology. While BCL2 CNVs were associated with increased expression, MYC CNVs were not. Furthermore, MYC and BCL2 CNVs, in the context of atypical double-hit, did not confer a similar gene expression profile as HGBL-DH/TH-BCL2. Finally, while MYC IHC has been proposed as a screening tool for FISH testing, two mechanisms were observed that uncoupled MYC rearrangement from IHC positivity. 1) low MYC mRNA expression and 2) false-negative immunohistochemistry (IHC) staining mediated by a single nucleotide polymorphism resulting in an asparagine to serine substitution at the 11th amino acid residue of MYC (MYC-N11S). Taken together, these results support the current exclusion of MYC and BCL2 CNVs from HGBL-DH/TH and highlight the ability of a molecular based classification system to identify tumors with shared biology that FISH and IHC fail to fully capture.

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Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽

10.1126/science.aba5257 ◽

2020 ◽

Vol 371 (6531) ◽

pp. eaba5257 ◽

Cited By ~ 2

Author(s):

Anna Kuchina ◽

Leandra M. Brettner ◽

Luana Paleologu ◽

Charles M. Roco ◽

Alexander B. Rosenberg ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cell Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Growth Stages ◽

High Throughput Analysis ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

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